Tootfinder

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 232 nodes and 1283194 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 332 nodes and 18613039 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 3920 nodes and 8053 edges.
Tags: Economic, Commerce, Weighted, Temporal

General Intuition, which trains AI agents in spatial reasoning using Medal.tv game clips, raised a $133.7M seed led by Khosla Ventures and General Catalyst (Rebecca Bellan/TechCrunch)
https://techcrunch.com/2025/10/16/gene

General Intuition lands $134M seed to teach agents spatial reasoning using video game clips | TechCrunch
Late last year, OpenAI reportedly tried to buy Medal and its vast trove of video game data for $500M. Today, the company spun out a frontier research lab that's using that data to build AI agents that understand how they move through space and time, a concept called spatial-temporal reasoning.

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique
https://arxiv.org/abs/2510.12076

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anoma…

Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making
Rifat Ara Proma, Ghulam Jilani Quadri, Paul Rosen
https://arxiv.org/abs/2510.11912

Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Prediction, and Decision-Making
Overplotted line charts can obscure trends in temporal data and hinder prediction. We conduct a user study comparing three alternatives-aggregated, trellis, and spiral line charts against standard line charts on tasks involving trend identification, making predictions, and decision-making. We found aggregated charts performed similarly to standard charts and support more accurate trend recognition and prediction; trellis and spiral charts generally lag. We also examined the impact on decision-m…

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 5505 nodes and 11719 edges.
Tags: Technological, Communication, Unweighted, Temporal

Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
toXiv_bot_toot

Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia)
Nidup Dorji, Sourav Das, Richard Stone, Alan R. Clough
https://arxiv.org/abs/2510.11101

Data Integration and spatio temporal statistics can quantify relative risk of medico-legal reforms: the example of police emergency mental health responses in Queensland (Australia)
This study examined the spatial-temporal dynamics of Emergency Examination Order or Authority (EE-O/A) admissions in Far Northern Queensland (FNQ) from 2009 to 2020, using 13,035 unique police records aggregated across 83 postcodes. A two-stage modelling framework was used: Lasso was used to identify a parsimonious set of socio economic and health-service covariates, and a Conditional Autoregressive (CAR) model incorporated these predictors with structured spatial and temporal random effects. T…

SatFusion: A Unified Framework for Enhancing Satellite IoT Images via Multi-Temporal and Multi-Source Data Fusion
Yufei Tong, Guanjie Cheng, Peihan Wu, Yicheng Zhu, Kexu Lu, Feiyi Chen, Meng Xi, Junqin Huang, Shuiguang Deng
https://arxiv.org/abs/2510.07905

SatFusion: A Unified Framework for Enhancing Satellite IoT Images via Multi-Temporal and Multi-Source Data Fusion
With the rapid advancement of the digital society, the proliferation of satellites in the Satellite Internet of Things (Sat-IoT) has led to the continuous accumulation of large-scale multi-temporal and multi-source images across diverse application scenarios. However, existing methods fail to fully exploit the complementary information embedded in both temporal and source dimensions. For example, Multi-Image Super-Resolution (MISR) enhances reconstruction quality by leveraging temporal compleme…

Tensor-based compression of the sea temperature data
Ilya Kosolapov, Tatiana Sheloput, Sergey Matveev
https://arxiv.org/abs/2510.09778 https://arxiv.org/pd…

Tensor-based compression of the sea temperature data
In this work we investigate efficient data compression for spatiotemporal Black, Azov and Marmara Seas temperature tensors that contain significant number of missing values. These tensors have a complex structure influenced by the coastlines and bathymetry, as well as temporal temperature changes. While such missing data typically provokes utilization of tensor completion algorithms, we demonstrate that standard SVD-based compression approaches (including the Tucker, Tensor-Train (TT) and Quant…

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki, Sabrina Kirrane
https://arxiv.org/abs/2510.04652

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage trac…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Maximum entropy temporal networks
Paolo Barucca

Predictive inference for time series: why is split conformal effective despite temporal dependence?
Rina Foygel Barber, Ashwin Pananjady
https://arxiv.org/abs/2510.02471 https:/…

Predictive inference for time series: why is split conformal effective despite temporal dependence?
We consider the problem of uncertainty quantification for prediction in a time series: if we use past data to forecast the next time point, can we provide valid prediction intervals around our forecasts? To avoid placing distributional assumptions on the data, in recent years the conformal prediction method has been a popular approach for predictive inference, since it provides distribution-free coverage for any iid or exchangeable data distribution. However, in the time series setting, the str…

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Davide Rucci, Emanuele Carlini, Patrizio Dazzi, Hanna Kavalionak, Matteo Mordacchini
https://arxiv.org/abs/2510.00758

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Key graph-based problems play a central role in understanding network topology and uncovering patterns of similarity in homogeneous and temporal data. Such patterns can be revealed by analyzing communities formed by nodes, which in turn can be effectively modeled through temporal $k$-cores. This paper introduces a novel decentralized and incremental algorithm for computing the core decomposition of temporal networks. Decentralized solutions leverage the ability of network nodes to communicate a…

Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model
Beige Jerry Jin, Leila Wehbe
https://arxiv.org/abs/2510.09415 ht…

Estimating Brain Activity with High Spatial and Temporal Resolution using a Naturalistic MEG-fMRI Encoding Model
Current non-invasive neuroimaging techniques trade off between spatial resolution and temporal resolution. While magnetoencephalography (MEG) can capture rapid neural dynamics and functional magnetic resonance imaging (fMRI) can spatially localize brain activity, a unified picture that preserves both high resolutions remains an unsolved challenge with existing source localization or MEG-fMRI fusion methods, especially for single-trial naturalistic data. We collected whole-head MEG when subjects…

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
Zhiyu Zheng, Shaoyu Chen, Haoran Yin, Xinbang Zhang, Jialv Zou, Xinggang Wang, Qian Zhang, Lefei Zhang
https://arxiv.org/abs/2510.08562

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
End-to-end autonomous driving (E2EAD) systems, which learn to predict future trajectories directly from sensor data, are fundamentally challenged by the inherent spatio-temporal imbalance of trajectory data. This imbalance creates a significant optimization burden, causing models to learn spurious correlations instead of causal inference, while also prioritizing uncertain, distant predictions, thereby compromising immediate safety. To address these issues, we propose ResAD, a novel Normalized R…

Do Railway Commuters Exhibit Consistent Route Choice Rationality Across Different Contexts and Time? Evidence from Tokyo metropolitan Commutes
Yixuan Y Zheng, Hideki Takayasu, Misako Takayasu
https://arxiv.org/abs/2510.12381

Do Railway Commuters Exhibit Consistent Route Choice Rationality Across Different Contexts and Time? Evidence from Tokyo metropolitan Commutes
Recent advances in data collection and technology enable a deeper understanding of complex urban commuting, yet few studies have rigorously analyzed the temporal stability and Origin-Destination (OD) heterogeneity of route choice. To address this, we analyze one year of smartphone position data from over one million users in the Tokyo metropolitan area to extract high-resolution commuting trajectories. Our methodology is twofold: First, we develop algorithms to process raw position data, accura…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 5038 nodes and 6325 edges.
Tags: Economic, Commerce, Weighted, Temporal

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Stefano Riva, Carolina Introini, Jos\`e Nathan Kutz, Antonio Cammi
https://arxiv.org/abs/2510.12368

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor
Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including unobservable fields, which is agnostic to sensor positions and able to handle noisy data through an ensemble strategy, leveraging the short training times and without the need for …

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Disharee Bhowmick, Ranjith Ramanathan, Sathyanarayanan N. Aakur
https://arxiv.org/abs/2510.09593

STaTS: Structure-Aware Temporal Sequence Summarization via Statistical Window Merging
Time series data often contain latent temporal structure, transitions between locally stationary regimes, repeated motifs, and bursts of variability, that are rarely leveraged in standard representation learning pipelines. Existing models typically operate on raw or fixed-window sequences, treating all time steps as equally informative, which leads to inefficiencies, poor robustness, and limited scalability in long or noisy sequences. We propose STaTS, a lightweight, unsupervised framework for …

Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
Victor Brabant, Angela Bonifati, R\'emy Cazabet
https://arxiv.org/abs/2510.00741 http…

Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
Community detection is a fundamental problem in network analysis, with many applications in various fields. Extending community detection to the temporal setting with exact temporal accuracy, as required by real-world dynamic data, necessitates methods specifically adapted to the temporal nature of interactions. We introduce LAGO, a novel method for uncovering dynamic communities by greedy optimization of Longitudinal Modularity, a specific adaptation of Modularity for continuous-time networks.…

The Fire We Share
Chen Wang, Mengtan Lin
https://arxiv.org/abs/2510.10841 https://arxiv.org/pdf/2510.10841

The Fire We Share
The Fire We Share proposes a care-centered, consequence-aware visualization framework for engaging with wildfire data not as static metrics, but as living archives of ecological and social entanglement. By combining plants-inspired data forms, event-based mapping, and narrative layering, the project foregrounds fire as a shared temporal condition-one that cuts across natural cycles and human systems. Rather than simplifying wildfire data into digestible visuals, The Fire We Share reimagines it …

A Spatio-temporal CP decomposition analysis of New England region in the US
Fatoumata Sanogo
https://arxiv.org/abs/2510.10322 https://arxiv.org/pdf/2510.10…

A Spatio-temporal CP decomposition analysis of New England region in the US
Spatio temporal data consist of measurement for one or more raster fields such as weather, traffic volume, crime rate, or disease incidents. Advances in modern technology have increased the number of available information for this type of data hence the rise of multidimensional data. In this paper we take advantage of the multidimensional structure of the data but also its temporal and spatial structure. In fact, we will be using the NCAR Climate Data Gateway website which provides data discove…

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski
https://arxiv.org/abs/2509.22360

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM's ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support analysis and training for the latter, we introduce CHRONOBERG, a temporally structured corpus of English book texts spanning 250 years, curat…

What Are We Clustering For? Establishing Performance Guarantees for Time Series Aggregation in Generation Expansion Planning
Luca Santosuosso, Bettina Klinz, Sonja Wogrin
https://arxiv.org/abs/2510.09357

What Are We Clustering For? Establishing Performance Guarantees for Time Series Aggregation in Generation Expansion Planning
Generation expansion planning (GEP) is a prominent example of capacity expansion problems in operations research. Being generally NP-hard, GEP optimization models can become intractable when nonconvex dynamics, time-coupling constraints, and complex asset interactions are involved. Time series aggregation (TSA) tackles this by reducing temporal complexity via input data clustering. However, existing TSA methods either focus solely on preserving the statistical features of the input data, yieldi…

Enhanced Hybrid Temporal Computing Using Deterministic Summations for Ultra-Low-Power Accelerators
Sachin Sachdeva, Jincong Lu, Wantong Li, Sheldon X. -D. Tan
https://arxiv.org/abs/2509.22999

Enhanced Hybrid Temporal Computing Using Deterministic Summations for Ultra-Low-Power Accelerators
This paper presents an accuracy-enhanced Hybrid Temporal Computing (E-HTC) framework for ultra-low-power hardware accelerators with deterministic additions. Inspired by the recently proposed HTC architecture, which leverages pulse-rate and temporal data encoding to reduce switching activity and energy consumption but loses accuracy due to its multiplexer (MUX)-based scaled addition, we propose two bitstream addition schemes: (1) an Exact Multiple-input Binary Accumulator (EMBA), which performs …

Bayesian Optimization of Multi-Bit Pulse Encoding in In2O3/Al2O3 Thin-film Transistors for Temporal Data Processing
Javier Meza-Arroyo, Benius Dunn, Weijie Xu, Yu-Chieh Chen, Jen-Sue Chen, Julia W. P. Hsu
https://arxiv.org/abs/2510.07421

Bayesian Optimization of Multi-Bit Pulse Encoding in In2O3/Al2O3 Thin-film Transistors for Temporal Data Processing
Utilizing the intrinsic history-dependence and nonlinearity of hardware, physical reservoir computing is a promising neuromorphic approach to encode time-series data for in-sensor computing. The accuracy of this encoding critically depends on the distinguishability of multi-state outputs, which is often limited by suboptimal and empirically chosen reservoir operation conditions. In this work, we demonstrate a machine learning approach, Bayesian optimization, to improve the encoding fidelity of …

Enigmatic centi-SFU and mSFU nonthermal radio transients detected in the middle corona
Surajit Mondal, Bin Chen, Sijie Yu, Xingyao Chen, Peijin Zhang, Dale Gary, Marin M. Anderson, Judd D. Bowman, Ruby Byrne, Morgan Catha, Sherry Chhabra, Larry D Addario, Ivey Davis, Jayce Dowell, Gregg Hallinan, Charlie Harnach, Greg Hellbourg, Jack Hickish, Rick Hobbs, David Hodge, Mark Hodges, Yuping Huang, Andrea Isella, Daniel C. Jacobs, Ghislain Kemby, John T. Klinefelter, Matthew Kolopanis, Niki…

Enigmatic centi-SFU and mSFU nonthermal radio transients detected in the middle corona
Decades of solar coronal observations have provided substantial evidence for accelerated particles in the corona. In most cases, the location of particle acceleration can be roughly identified by combining high spatial and temporal resolution data from multiple instruments across a broad frequency range. In almost all cases, these nonthermal particles are associated with quiescent active regions, flares, and coronal mass ejections (CMEs). Only recently, some evidence of the existence of nonther…

from my link log —
LIGO can detect daylight savings time.
https://arxiv.org/abs/2509.11849
saved 2025-09-29 https://dotat.at/:/TJ9D3.html

Can LIGO Detect Daylight Savings Time?
Yes, it can. Catalogs produced by networks of Gravitational-wave interferometers are subject to complicated selection effects, and the gold-standard remains direct measurements of the detection probability through large injection campaigns. I leverage public data products from the LIGO-Virgo-KAGRA Collaborations' 3rd and 4th observing runs to show that there are non-trivial temporal variations within the detection probability that are well-described by a weekly cycle. There are clear differen…

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 6235 nodes and 13326 edges.
Tags: Technological, Communication, Unweighted, Temporal

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data
Yi Hu, Zheyuan Cheng
https://arxiv.org/abs/2509.22795 https://

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data
Reliable detection and classification of power system events are critical for maintaining grid stability and situational awareness. Existing approaches often depend on limited labeled datasets, which restricts their ability to generalize to rare or unseen disturbances. This paper proposes a novel framework that integrates generative modeling, sliding-window temporal processing, and decision fusion to achieve robust event detection and classification using synchrophasor data. A variational autoe…

A human EEG dataset for multisensory perception and mental imagery #YOTO (You Only Think Once)

A Human EEG Dataset for Multisensory Perception and Mental Imagery - Scientific Data
The YOTO (You Only Think Once) dataset presents a human electroencephalog- raphy (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 26 participants who performed tasks involving both unimodal and multimodal stimuli. Researchers collected high-resolution EEG signals at a 1000 Hz sampling rate to capture high-temporal-resolution neural activity related to internal mental representations. The protocol incorporated visual, auditory, and combined cues to in…

GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint)
Tianxiang Xu, Zhichao Wen, Xinyu Zhao, Qi Hu, Yan Li, Chang Liu
https://arxiv.org/abs/2510.07285

GTCN-G: A Residual Graph-Temporal Fusion Network for Imbalanced Intrusion Detection (Preprint)
The escalating complexity of network threats and the inherent class imbalance in traffic data present formidable challenges for modern Intrusion Detection Systems (IDS). While Graph Neural Networks (GNNs) excel in modeling topological structures and Temporal Convolutional Networks (TCNs) are proficient in capturing time-series dependencies, a framework that synergistically integrates both while explicitly addressing data imbalance remains an open challenge. This paper introduces a novel deep le…

Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
https://arxiv.org/abs/2510.06149 https://arxiv.…

Implicit Updates for Average-Reward Temporal Difference Learning
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD($λ$) is highly sensitive to the choice of step-size and thus requires careful tuning to maintain numerical stability. We introduce average-reward implicit TD($λ$), which employs an implicit fixed point update to provide data-adaptive stabilization while preserving the per iteration computational complexity of standard average-reward TD($λ$). In contrast to prior finite-tim…

Replaced article(s) found for cs.DS. https://arxiv.org/list/cs.DS/new
[1/1]:
- Minimizing Reachability Times on Temporal Graphs via Shifting Labels
Argyrios Deligkas, Eduard Eiben, George Skretas

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
Lingyu Zhang, Guobin Wu, Yan Wang, Pengfei Xu, Jian Liang, Xuan Song, Yunhai Wang
https://arxiv.org/abs/2509.22661

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
The next Point-of-interest (POI) recommendation is mainly based on sequential traffic information to predict the user's next boarding point location. This is a highly regarded and widely applied research task in the field of intelligent transportation, and there have been many research results to date. Traditional POI prediction models primarily rely on short-term traffic sequence information, often neglecting both long-term and short-term preference data, as well as crucial spatiotemporal cont…

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Austin Feng, Andreas Varvarigos, Ioannis Panitsas, Daniela Fernandez, Jinbiao Wei, Yuwei Guo, Jialin Chen, Ali Maatouk, Leandros Tassiulas, Rex Ying
https://arxiv.org/abs/2510.06063

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as weather, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets are underrepresented in public benchmarks due to proprietary restrictions. Existing datasets are often anonymized and normalized, removing scale information and limi…

Integrating Weather and Land Cover Data into Geospatial Impact Evaluations
Elinor Benami, Mike Cecil, Anna Josephson, Gina Maskell, Jeffrey D. Michler
https://arxiv.org/abs/2510.05108

Integrating Weather and Land Cover Data into Geospatial Impact Evaluations
Integrating gridded weather and earth observation data into impact evaluations holds great promise. It allows researchers to capture environmental context, external shocks, and even to measure outcomes (e.g., land cover change, agricultural production) that surveys might miss due to spatial or temporal data collection constraints. However, with great power comes great responsibility: the increasing ease of extracting time series from these datasets belies potentially complex geospatial and meas…

Optimizing the Network Topology of a Linear Reservoir Computer
Sahand Tangerami, Nicholas A. Mecholsky, Francesco Sorrentino
https://arxiv.org/abs/2509.23391 https://

Optimizing the Network Topology of a Linear Reservoir Computer
Machine learning has become a fundamental approach for modeling, prediction, and control, enabling systems to learn from data and perform complex tasks. Reservoir computing is a machine learning tool that leverages high-dimensional dynamical systems to efficiently process temporal data for prediction and observation tasks. Traditionally, the connectivity of a reservoir computer (RC) is generated at random, lacking a principled design. Here, we focus on optimizing the topology of a linear RC to …

GrifFinNet: A Graph-Relation Integrated Transformer for Financial Predictions
Chenlanhui Dai, Wenyan Wang, Yusi Fan, Yueying Wang, Lan Huang, Kewei Li, Fengfeng Zhou
https://arxiv.org/abs/2510.10387

GrifFinNet: A Graph-Relation Integrated Transformer for Financial Predictions
Predicting stock returns remains a central challenge in quantitative finance, transitioning from traditional statistical methods to contemporary deep learning techniques. However, many current models struggle with effectively capturing spatio-temporal dynamics and integrating multiple relational data sources. This study proposes GrifFinNet, a Graph-Relation Integrated Transformer for Financial Predictions, which combines multi-relational graph modeling with Transformer-based temporal encoding. …

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 1437 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
David Berghaus, Patrick Seifner, Kostadin Cvejoski, Ramses J. Sanchez
https://arxiv.org/abs/2510.12640

On Foundation Models for Temporal Point Processes to Accelerate Scientific Discovery
Many scientific fields, from medicine to seismology, rely on analyzing sequences of events over time to understand complex systems. Traditionally, machine learning models must be built and trained from scratch for each new dataset, which is a slow and costly process. We introduce a new approach: a single, powerful model that learns the underlying patterns of event data in context. We trained this "foundation model" on millions of simulated event sequences, teaching it a general-purpose understa…

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 188508 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

FreeViS: Training-free Video Stylization with Inconsistent References
Jiacong Xu, Yiqun Mei, Ke Zhang, Vishal M. Patel
https://arxiv.org/abs/2510.01686 https://

FreeViS: Training-free Video Stylization with Inconsistent References
Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our…

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 668 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Edit-Based Flow Matching for Temporal Point Processes
David L\"udke, Marten Lienen, Marcel Kollovieh, Stephan G\"unnemann
https://arxiv.org/abs/2510.06050 https://

Edit-Based Flow Matching for Temporal Point Processes
Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that are limited by their sequential sampling. Recent non-autoregressive, diffusion-style models mitigate these issues by jointly interpolating between noise and data through event insertions and deletions in a discrete Markov chain. In this work, we generalize this perspective and introduce an Edit Flow process for TPPs th…

Fuzzy Jump Models for Soft and Hard Clustering of Multivariate Time Series Data
Federico P. Cortese, Antonio Pievatolo, Elisa Maria Alessi
https://arxiv.org/abs/2509.26029 https…

Fuzzy Jump Models for Soft and Hard Clustering of Multivariate Time Series Data
Statistical jump models have been recently introduced to detect persistent regimes by clustering temporal features and discouraging frequent regime changes. However, they are limited to hard clustering and thereby do not account for uncertainty in state assignments. This work presents an extension of the statistical jump model that incorporates uncertainty estimation in cluster membership. Leveraging the similarities between statistical jump models and the fuzzy c-means framework, our fuzzy jum…

Crosslisted article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- Evaluating Line Chart Strategies for Mitigating Density of Temporal Data: The Impact on Trend, Pr...
Rifat Ara Proma, Ghulam Jilani Quadri, Paul Rosen

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 4409 nodes and 8938 edges.
Tags: Technological, Communication, Unweighted, Temporal

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

Chronologically Consistent Generative AI
Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
https://arxiv.org/abs/2510.11677 https://arxiv.org/pdf/2510.11677

Chronologically Consistent Generative AI
We introduce a family of chronologically consistent, instruction-following large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the s…

Label-frugal satellite image change detection with generative virtual exemplar learning
Hichem Sahbi
https://arxiv.org/abs/2510.06926 https://arxiv.org/pdf…

Label-frugal satellite image change detection with generative virtual exemplar learning
Change detection is a major task in remote sensing which consists in finding all the occurrences of changes in multi-temporal satellite or aerial images. The success of existing methods, and particularly deep learning ones, is tributary to the availability of hand-labeled training data that capture the acquisition conditions and the subjectivity of the user (oracle). In this paper, we devise a novel change detection algorithm, based on active learning. The main contribution of our work resides …

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 502 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

LDMD with Temporally Adaptive Segmentation
Qiuqi Li, Chang Liu, Yifei Yang
https://arxiv.org/abs/2510.08065 https://arxiv.org/pdf/2510.08065

LDMD with Temporally Adaptive Segmentation
Dynamic mode decomposition (DMD) is a widely used data-driven algorithm for predicting the future states of dynamical systems. However, its standard formulation often struggles with poor long-term predictive accuracy. To address this limitation, we propose a localized DMD (LDMD) framework that improves prediction performance by integrating DMD's strong linear forecasting capabilities with time-domain segmentation techniques. In this framework, the temporal domain is segmented into multiple subi…

Preference-Based Dynamic Ranking Structure Recognition
Nan Lu, Jian Shi, Xin-Yu Tian
https://arxiv.org/abs/2509.24493 https://arxiv.org/pdf/2509.24493

Preference-Based Dynamic Ranking Structure Recognition
Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic…

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 668 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Shiyuan Luo, Runlong Yu, Shengyu Chen, Yingda Fan, Yiqun Xie, Yanhua Li, Xiaowei Jia
https://arxiv.org/abs/2510.09500

Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to varying spatial regions and scales due to the inherent data heterogeneity presented in real environmental ecosystems. This generalization issue is further exacerbated by the limited observation samples available for model training. To address these issues, we propose Geo-STARS, a geo-aware spatio-temporal modeling framewo…

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 502 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi
https://arxiv.org/abs/2510.05060 https…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive retraining when the underlying data distribution changes. To overcome these limitations, we propose Reservoir Conformal Prediction (ResCP), a…

Multi-Scale Land Use Impacts on Fossil Fuel-Related CO$_2$ Emissions in the United States
Jason Hawkins, Mehrnoosh Zare
https://arxiv.org/abs/2510.08611 https://

Multi-Scale Land Use Impacts on Fossil Fuel-Related CO$_2$ Emissions in the United States
Anthropogenic greenhouse gas (GHG) emissions exhibit spatial variation owing to differences in development patterns, local climate, economic composition, energy sources, and other factors. Many of these factors - and therefore their contribution to GHG production - are influenceable through spatial planning and economic policy. Recent advances in environmental data reporting and climate flux measurement have produced high fidelity GHG emissions estimates at detailed spatial and temporal resolut…

How Far Do Time Series Foundation Models Paint the Landscape of Real-World Benchmarks ?
Lujun Li, Lama Sleem, Yiqun Wang, Yangjie Xu, Niccol\`o Gentile, Radu State
https://arxiv.org/abs/2509.26347

How Far Do Time Series Foundation Models Paint the Landscape of Real-World Benchmarks ?
Recent evaluations of time-series foundation models (TSFMs) have emphasized synthetic benchmarks, leaving real-world generalization less thoroughly examined. This work proposes a novel benchmarking approach that bridges synthetic and realistic data by extracting temporal signals from real-world video using optical flow and curating datasets reflecting everyday temporal dynamics. Building upon this pipeline, we introduce REAL-V-TSFM, a novel dataset designed to capture rich and diverse time seri…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 1629 nodes and 3865 edges.
Tags: Economic, Commerce, Weighted, Temporal

Seeing Space and Motion: Enhancing Latent Actions with Spatial and Dynamic Awareness for VLA
Zhejia Cai, Yandan Yang, Xinyuan Chang, Shiyi Liang, Ronghan Chen, Feng Xiong, Mu Xu, Ruqi Huang
https://arxiv.org/abs/2509.26251

Seeing Space and Motion: Enhancing Latent Actions with Spatial and Dynamic Awareness for VLA
Latent Action Models (LAMs) enable Vision- Language-Action (VLA) systems to learn semantic action rep- resentations from large-scale unannotated data. Yet, we identify two bottlenecks of LAMs: 1) the commonly adopted end-to-end trained image encoder suffers from poor spatial understanding; 2) LAMs can be fragile when input frames are distant, leading to limited temporal perception. Such factors inevitably hinder stable and clear action modeling. To this end, we propose Farsighted-LAM, a latent …

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 19438 nodes and 23191 edges.
Tags: Economic, Commerce, Weighted, Temporal

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method
Lulu Gong, Shreya Saxena
https://arxiv.org/abs/2510.06091 https://arxiv.o…

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method
Mixtures of linear dynamical systems (MoLDS) provide a path to model time-series data that exhibit diverse temporal dynamics across trajectories. However, its application remains challenging in complex and noisy settings, limiting its effectiveness for neural data analysis. Tensor-based moment methods can provide global identifiability guarantees for MoLDS, but their performance degrades under noise and complexity. Commonly used expectation-maximization (EM) methods offer flexibility in fitting…

Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Songyuan Sui, Zihang Xu, Yu-Neng Chuang, Kwei-Herng Lai, Xia Hu
https://arxiv.org/abs/2510.05950

Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and inflexible. Recent reasoning-oriented large language models (LLMs) show promise in understanding temporal patterns, but purely zero-shot usage remains suboptimal. We propose FETA, a multi-agent framework for training-free TSC via exemplar-based in-context reasoning. FETA decomposes a multivariate series into channel-wise subproblems, retrieves a few s…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani
https://arxiv.org/abs/2510.01184 htt…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the training distribution. We build on the observation that these models leverage (learned) score functions of noisy data distributions for sampling and show that rescaling these allows one to effectively control a `local' sampling temperature. Notably, this approach does not require any finetuning or alterations to training…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 502 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Yukun Yang
https://arxiv.org/abs/2509.26145

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Depression is a major global public health challenge and its early identification is crucial. Social media data provides a new perspective for depression detection, but existing methods face limitations such as insufficient accuracy, insufficient utilization of time series features, and high annotation costs. To this end, this study proposes the LMILAtt model, which innovatively integrates Long Short-Term Memory autoencoders and attention mechanisms: firstly, the temporal dynamic features of us…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 242 nodes and 6594492 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Guillem Capellera, Luis Ferraz, Antonio Rubio, Alexandre Alahi, Antonio Agudo
https://arxiv.org/abs/2509.22522

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they interact synchronously. To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to unify these two processes by simultaneously generating continuous spatio-temporal data and synchronous discrete events. We demonstrate its efficacy in the sports domain by simultaneously modeling multi-agent trajectories and key possession events. …

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 3271 nodes and 6246 edges.
Tags: Technological, Communication, Unweighted, Temporal

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 232 nodes and 1283194 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 332 nodes and 18613039 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
Zeqi Ye, Minshuo Chen
https://arxiv.org/abs/2510.02216 https://

Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
Imputation methods play a critical role in enhancing the quality of practical time-series data, which often suffer from pervasive missing values. Recently, diffusion-based generative imputation methods have demonstrated remarkable success compared to autoregressive and conventional statistical approaches. Despite their empirical success, the theoretical understanding of how well diffusion-based models capture complex spatial and temporal dependencies between the missing values and observed ones…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 10215 nodes and 11837 edges.
Tags: Economic, Commerce, Weighted, Temporal

MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
Fanjin Meng, Yuan Yuan, Jingtao Ding, Jie Feng, Chonghua Han, Yong Li
https://arxiv.org/abs/2509.22403

MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning
Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale and semantic understanding. While Large Language Models (LLMs) offer powerful semantic reasoning, they lack the innate understanding of spatio-temporal statistics required for generating physically plausible mobility trajectories. To address these gaps, we propose MoveFM-R, a novel framework that unlocks the full potential of mobility foundation model…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 1619 nodes and 3197 edges.
Tags: Economic, Commerce, Weighted, Temporal

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 2586 nodes and 3548 edges.
Tags: Economic, Commerce, Weighted, Temporal

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 4330 nodes and 8711 edges.
Tags: Technological, Communication, Unweighted, Temporal

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 403 nodes and 1417485 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 2948 nodes and 5515 edges.
Tags: Technological, Communication, Unweighted, Temporal

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 3971 nodes and 7604 edges.
Tags: Technological, Communication, Unweighted, Temporal

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 4499 nodes and 4891 edges.
Tags: Economic, Commerce, Weighted, Temporal

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 4695 nodes and 5118 edges.
Tags: Economic, Commerce, Weighted, Temporal

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 2728 nodes and 3204 edges.
Tags: Economic, Commerce, Weighted, Temporal