Tootfinder

A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14655 https:…

A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
In this article we propose an extension to the typed natural deduction calculus TNDPQ to model verification of counterfactual fairness in probabilistic classifiers. This is obtained formulating specific structural conditions for causal labels and checking that evaluation is robust under their variation.

Good news, everyone!
https://www.gsmarena.com/eu_energy_labels_smartphones_mandatory_today_battery_life_durability_repairability-news-68345.php

The Labeled Coupon Collector Problem
Andrew Tan, Oriel Limor, Daniella Bar-Lev, Ryan Gabrys, Zohar Yakhini, Paul H. Siegel
https://arxiv.org/abs/2507.15231

The Labeled Coupon Collector Problem
We generalize the well-known Coupon Collector Problem (CCP) in combinatorics. Our problem is to find the minimum and expected number of draws, with replacement, required to recover $n$ distinctly labeled coupons, with each draw consisting of a random subset of $k$ different coupons and a random ordering of their associated labels. We specify two variations of the problem, Type-I in which the set of labels is known at the start, and Type-II in which the set of labels is unknown at the start. We …

A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14650

A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
In this article we propose an extension to the typed natural deduction calculus TNDPQ to model verification of individual fairness and intersectionality in probabilistic classifiers. Their interpretation is obtained by formulating specific conditions for the application of the structural rule of Weakening. Such restrictions are given by causal labels used to check for conditional independence between protected and target variables.

Sind die „Boomer“ wirklich schuld, Millennials nur verweichlicht und Gen Z zu anspruchsvoll? Der SWR-Artikel über die Untersuchungen der Soziologin Katja Schmid räumt auf: Generationenschubladen helfen wenig, verstärken aber Vorurteile und Diskriminierung. Unterschiede gibt’s, aber die verlaufen meist quer durch alle Jahrgänge – nicht entlang erfundener Labels - Boomer, Millenials, Gen Z, Alpha? Warum es keine "Generationen" gibt

Boomer, Millenials, Gen Z, Alpha? Warum es keine "Generationen" gibt
Die Boomer haben die Welt zerstört, die Millennials sind verweichlicht, die Gen Z viel zu anspruchsvoll. Seit Jahrzehnten labeln Gesellschaften Alterskohorten in bestimmte "Generationen". Doch was ist dran an solchen Zuschreibungen? Nützen uns diese Schubladen?

Finally cancelled my Ideogram subscription today, I've had it for the past 12 months and loved it.
Now I'm using ChatGPT 4o almost exclusively for my AI image generation needs.
I updated my paid subscriptions list:
https://wiki.wesfryer.com/subscriptions

wesfryer - Subscriptions
Thoughts on Subscriptions: Have you noticed lots of companies and people want you to SUBSCRIBE to their service or commit to a MONTHLY payment / fee? Services like TRIM can help us identify, remember and manage our subscriptions. (s/o to Jason Neiffer in EdTechSR Ep 235) It can be helpful to SHARE

Label Unification for Cross-Dataset Generalization in Cybersecurity NER
Maciej Jalocha, Johan Hausted Schmidt, William Michelseen
https://arxiv.org/abs/2507.13870

Label Unification for Cross-Dataset Generalization in Cybersecurity NER
The field of cybersecurity NER lacks standardized labels, making it challenging to combine datasets. We investigate label unification across four cybersecurity datasets to increase data resource usability. We perform a coarse-grained label unification and conduct pairwise cross-dataset evaluations using BiLSTM models. Qualitative analysis of predictions reveals errors, limitations, and dataset differences. To address unification limitations, we propose alternative architectures including a mult…

Folllowing the FAQ and blog, I thought I was ... stupid?
You can add labels in Draw.io - easy:
https://www.drawio.com/doc/faq/labels-add
And you should be able to rotate them, say, to fit an angled line... right?

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
Maximilian Rokuss, Benjamin Hamm, Yannick Kirchhoff, Klaus Maier-Hein
https://arxiv.org/abs/2507.13830

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
We introduce the first publicly available breast MRI dataset with explicit left and right breast segmentation labels, encompassing more than 13,000 annotated cases. Alongside this dataset, we provide a robust deep-learning model trained for left-right breast segmentation. This work addresses a critical gap in breast MRI analysis and offers a valuable resource for the development of advanced tools in women's health. The dataset and trained model are publicly available at: www.github.com/MIC-DKFZ…

Dvorak-Dell-Grohe-Rattan theorem via an asymptotic argument
Alexander Kozachinskiy
https://arxiv.org/abs/2507.14669 https://arxiv.org…

Dvorak-Dell-Grohe-Rattan theorem via an asymptotic argument
Two graphs $G_1,G_2$ are distinguished by the Weisfeiler--Leman isomorphism test if and only if there is a tree $T$ that has a different number of homomorphisms to $G_1$ and to $G_2$. There are two known proofs of this fact -- a logical proof by Dvorak and a linear-algebraic proof by Dell, Grohe, and Rattan. We give another simple proof, based on ordering WL-labels and asymptotic arguments.

Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
Hope Schroeder, Deb Roy, Jad Kabbara
https://arxiv.org/abs/2507.15821

Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
LLM use in annotation is becoming widespread, and given LLMs' overall promising performance and speed, simply "reviewing" LLM annotations in interpretive tasks can be tempting. In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution, impacting both the evaluation of LLM performance, and analysis using these labels in a social science task downstream. We conducted a pre-registered experiment with 410 unique annotators and over 7,000…

Why does the #BBC use terms like “Iran-backed Houthis” or “Hamas-run hospital,” but avoid labels such as “US-backed IDF” or referring to Israeli Prime Minister Benjamin Netanyahu as an “indicted war criminal.”?
'Why 'Hamas-run hospital' but never ‘US-backed IDF’?': Zohran Mamdani calls out double standards on Israel-Gaza coverage; slams BBC - Times of India

'Why 'Hamas-run hospital' but never ‘US-backed IDF’?': Zohran Mamdani calls out double standards on Israel-Gaza coverage; slams BBC - Times of India
US News: Democratic mayoral candidate Zohran Mamdani has accused the BBC of biased reporting on the Israel-Gaza conflict, criticizing their selective language.

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches
Ranindya Paramitha, Yuan Feng, Fabio Massacci
https://arxiv.org/abs/2506.11939

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches
Vulnerability datasets used for ML testing implicitly contain retrospective information. When tested on the field, one can only use the labels available at the time of training and testing (e.g. seen and assumed negatives). As vulnerabilities are discovered across calendar time, labels change and past performance is not necessarily aligned with future performance. Past works only considered the slices of the whole history (e.g. DiverseVUl) or individual differences between releases (e.g. Jimene…

#GenderFluid #GenderSolid #GenderFlux #Gender

Label free sub-diffraction imaging using non-linear photon avalanche backlight
Suresh Karmegam, Marcin Szalkowski, Malgorzata Misiak, Katarzyna Prorok, Damian Szyma\'nski, Artur Bednarkiewicz
https://arxiv.org/abs/2507.14667

Label free sub-diffraction imaging using non-linear photon avalanche backlight
Optical imaging below the limit of light diffraction offers an unprecedented opportunity to study outlook, organization, interactions or in-situ functioning of sub-micrometer, highly transparent objects such as subcellular structures in vitro, thin layers or nano-engineered devices. However, most of current methodologies require to use specially designed luminescent labels, which not only may affect the properties of the sample itself, but often are (photo)toxic, susceptible to photobleaching, …

Earlier this month, #TomPatterson released a free (public domain) print-quality physical map of maritime Southeast Asia. It could actually almost double as a map of SEA except the northern part of Myanmar is cut off. 🗺️
https://

Tony Romo debates 'dynasty is over' labels for Chiefs, explains why Kansas City should worry opposition

https://www.cbssports.com/nfl/news/tony-ro

Probably Approximately Correct Labels
Emmanuel J. Cand\`es, Andrew Ilyas, Tijana Zrnic
https://arxiv.org/abs/2506.10908 https://arxiv…

Probably Approximately Correct Labels
Obtaining high-quality labeled datasets is often costly, requiring either extensive human annotation or expensive experiments. We propose a method that supplements such "expert" labels with AI predictions from pre-trained models to construct labeled datasets more cost-effectively. Our approach results in probably approximately correct labels: with high probability, the overall labeling error is small. This solution enables rigorous yet efficient dataset curation using modern AI models. We demon…

ISP Frontier Communications settles a lawsuit from record labels that demanded broadband users accused of piracy be dropped; SCOTUS may hear Cox's similar case (Jon Brodkin/Ars Technica)
https://arstechnica.com/tech-policy/20

ISP settles with record labels that demanded mass termination of Internet users
One piracy case is over, but the biggest one could be decided by Supreme Court.

ISP Frontier Communications settles a lawsuit from record labels that demanded broadband users accused of piracy be dropped; SCOTUS may hear Cox's similar case (Jon Brodkin/Ars Technica)
https://arstechnica.com/tech-policy/20

ISP settles with record labels that demanded mass termination of Internet users
One piracy case is over, but the biggest one could be decided by Supreme Court.

Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
Celso Fran\c{c}a, Gestefane Rabbi, Thiago Salles, Washington Cunha, Leonardo Rocha, Marcos Andr\'e Gon\c{c}alves
https://arxiv.org/abs/2507.03761

Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
In the context of Extreme Multi-label Text Classification (XMTC), where labels are assigned to text instances from a large label space, the long-tail distribution of labels presents a significant challenge. Labels can be broadly categorized into frequent, high-coverage \textbf{head labels} and infrequent, low-coverage \textbf{tail labels}, complicating the task of balancing effectiveness across all labels. To address this, combining predictions from multiple retrieval methods, such as sparse re…

Neue EU-Labels: Apple kritisiert Testmethoden, stuft iPhone und iPad herunter
In einem Paper erläutert Apple, wie die Firma mit den neuen Pflichtlabels der EU-Kommission umgeht. Das iPhone bekommt ein "B", mehrere iPads gar ein "G".

This https://arxiv.org/abs/2504.11284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal Area Under the ROC Curve (AUC) against a single binary target label. However, one may often observe multiple binary target labels, e.g., from distinct human annotators. How can one synthesize such labels into a single coherent ranking? In this work, we formally analyze two approaches to this problem -- loss aggregation and label aggregation -- by characterizing their B…

Now out in #TMLR:
🍇 GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks 🍇
There's lots of work on sampling subgraphs for GNNs, but relatively little on making this sampling process _adaptive_. That is, learning to select the data from the graph that is relevant for your task.
We introduce an RL-based and a GFLowNet-based sampler and show that the approach perf…

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin, Xuanjie Liu, Gus Xia
https://arxiv.org/abs/2506.15548

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality itself, e.g., as sequences of symbolic notes. This observation enables both understanding tasks (e.g., chord recognition) and conditional generation tasks (e.g., chord-conditioned melody generation) to be unified under a music-for-music sequence modeling paradigm. In this work, we propose parameter-efficient solutions fo…

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

"Switzerland Rolls Out Labels Flagging Animal Suffering In Food Products"
#Switzerland #Animals #Food

Switzerland Rolls Out Labels Flagging Animal Suffering In Food Products
Switzerland is tightening up its food labeling by requiring animal products linked to "painful procedures" to include this information

Doritos, M&Ms Could Be Forced to Include Warning Labels in Texas (Bloomberg)
https://www.bloomberg.com/news/articles/2025-06-02/doritos-m-ms-could-be-forced-to-include-warning-labels-in-texas
http://www.memeorandum.com/250602/p147#a250602p147

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Niran Nataraj, Maina Sogabe, Kenji Kawashima
https://arxiv.org/abs/2506.14303

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propose orGAN, a GAN-based system for generating high-fidelity, annotated surgical images of bleeding. By leveraging small "mimicking organ" datasets, synthetic models that replicate t…

Attributes Shape the Embedding Space of Face Recognition Models
Pierrick Leroy, Antonio Mastropietro, Marco Nurisso, Francesco Vaccarino
https://arxiv.org/abs/2507.11372

Attributes Shape the Embedding Space of Face Recognition Models
Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a …

« Exemples de labels (#GitLab) de gestion de projet »
https://notes.sklein.xyz/2025-05-13_0938/zen/

GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
Zhaoyu Xing, Yang Wan, Juan Wen, Wei Zhong
https://arxiv.org/abs/2507.10956

GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering
It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algori…

Lukashenko regime designates exiled Belarusian opposition body as 'terrorist organization': https://benborges.xyz/2025/07/10/lukashenko-regime-designates-exiled-belarusian.html

The Casimir eigenvalues on $ad^{\otimes k}$ of SU(N) are linear on N
R. L. Mkrtchyan
https://arxiv.org/abs/2506.13062 https://arxiv.o…

The Casimir eigenvalues on $ad^{\otimes k}$ of SU(N) are linear on N
We consider eigenvalues of the Casimir operator on the naturally defined \textit{stable sequences} of representations of $su(N)$ algebra and prove that eigenvalues are linear over $N$ iff $λ_1+2λ_2+...+kλ_k=λ_{N-1}+2λ_{N-2}+...+kλ_{N-k}$, where $λ_i$ are Dynkin labels, and $λ_i=0$ for $k

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Chihiro Yamasaki, Kai Sugahara, Yuma Nagi, Kazushi Okamoto
https://arxiv.org/abs/2507.03945

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Complementary recommendations enhance the user experience by suggesting items that are frequently purchased together while serving different functions from the query item. Inferring or evaluating whether two items have a complementary relationship requires complementary relationship labels; however, defining these labels is challenging because of the inherent ambiguity of such relationships. Complementary labels based on user historical behavior logs attempt to capture these relationships, but …

Near-Optimal Vertex Fault-Tolerant Labels for Steiner Connectivity
Koustav Bhanja, Asaf Petruschka
https://arxiv.org/abs/2506.23215 https://

Near-Optimal Vertex Fault-Tolerant Labels for Steiner Connectivity
We present a compact labeling scheme for determining whether a designated set of terminals in a graph remains connected after any $f$ (or less) vertex failures occur. An $f$-FT Steiner connectivity labeling scheme for an $n$-vertex graph $G=(V,E)$ with terminal set $U \subseteq V$ provides labels to the vertices of $G$, such that given only the labels of any subset $F \subseteq V$ with $|F| \leq f$, one can determine if $U$ remains connected in $G-F$. The main complexity measure is th…

Them: you should probably use the keyboard layout that matches the actual labels on your keyboard
Me: …

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models
Chenchen Yuan, Zheyu Zhang, Shuo Yang, Bardh Prenkaj, Gjergji Kasneci
https://arxiv.org/abs/2506.14625

Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models
Large Language Models (LLMs) have shown impressive moral reasoning abilities. Yet they often diverge when confronted with complex, multi-factor moral dilemmas. To address these discrepancies, we propose a framework that synthesizes multiple LLMs' moral judgments into a collectively formulated moral judgment, realigning models that deviate significantly from this consensus. Our aggregation mechanism fuses continuous moral acceptability scores (beyond binary labels) into a collective probability,…

This https://arxiv.org/abs/2506.02763 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated catalog of stellar labels derived from LAMOST low-resolution spectra in a physics-sensible and rigorous manner with DD-Payne, taking labels from high-resolution spectroscopy as training set. The catalog contains atmospheric parameters for 6.4 million stars re…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
Konstantin Avrachenkov, Diego Goldsztajn
https://arxiv.org/abs/2506.09223 htt…

Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
We consider graphs with two communities and analyze an algorithm for learning the community labels when the edges of the graph and only a small fraction of the labels are known in advance. The algorithm is based on the Glauber dynamics for an Ising model where the energy function includes a quadratic penalty on the magnetization. The analysis focuses on graphs sampled from a Stochastic Block Model (SBM) with slowly growing mean degree. We derive a mean-field limit for the magnetization of each …

A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
Kayhan Latifzadeh, Jacek Gwizdka, Luis A. Leiva
https://arxiv.org/abs/2507.08003

A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages
We contribute a comprehensive dataset to study user attention and purchasing behavior on Search Engine Result Pages (SERPs). Previous work has relied on mouse movements as a low-cost large-scale behavioral proxy but also has relied on self-reported ground-truth labels, collected at post-task, which can be inaccurate and prone to biases. To address this limitation, we use an eye tracker to construct an objective ground-truth of continuous visual attention. Our dataset comprises 2,776 transaction…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
Datong Zhou
https://arxiv.org/abs/2506.13587 https…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
We develop a mean-field theory for large, non-exchangeable particle (agent) systems where the states and interaction weights co-evolve in a coupled system of SDEs. A first main result is a generalization of the propagation of chaos. The weight adaptation in the SDEs makes the classical approach of using a static probability space as the continuum limit for the agent labels inadequate. We address this by introducing a label space endowed with a filtration that captures the stochasticity. While t…

This https://arxiv.org/abs/2307.10434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csFL_…

Learning Formal Specifications from Membership and Preference Queries
Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only. …

NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
Dzung Dinh, Boqi Chen, Marc Niethammer, Junier Oliva
https://arxiv.org/abs/2507.12412

NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
In many critical applications, resource constraints limit the amount of information that can be gathered to make predictions. For example, in healthcare, patient data often spans diverse features ranging from lab tests to imaging studies. Each feature may carry different information and must be acquired at a respective cost of time, money, or risk to the patient. Moreover, temporal prediction tasks, where both instance features and labels evolve over time, introduce additional complexity in dec…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Mohammad R. Salmanpour, Amir Hossein Pouria, Sonia Falahati, Shahram Taeb, Somayeh Sadat Mehrnia, Ali Fathi Jouzdani, Mehrdad Oveisi, Ilker Hacihaliloglu, Arman Rahmim
https://arxiv.org/abs/2507.0818…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled datasets, limiting their real-world application in settings with scarce annotations. Methods: We analyzed CT scans from 977 patients across 12 datasets extracting 1218 radiomics features using Laplacian of Gaussian and wavelet filters via PyRadiomics Dimensionality reduction was applied with 56 feature selection and ex…

Archaic Podcast 572 - Dave Black
Bio.
Dave black is a Spanish electronic musician and dj specializing in techno. His sound is deeply influenced by both past and present electronic music, blending hypnotic rhythms with raw energy. throughout his career, he has been affiliated with renowned labels such as Illegal Alien, IuminalRecordings, Involve, Utopia, and his own label, Philia Records.
[…]

Flexible and Efficient Drift Detection without Labels
Nelvin Tan, Yu-Ching Shih, Dong Yang, Amol Salunkhe
https://arxiv.org/abs/2506.08734 https://

Flexible and Efficient Drift Detection without Labels
Machine learning models are being increasingly used to automate decisions in almost every domain, and ensuring the performance of these models is crucial for ensuring high quality machine learning enabled services. Ensuring concept drift is detected early is thus of the highest importance. A lot of research on concept drift has focused on the supervised case that assumes the true labels of supervised tasks are available immediately after making predictions. Controlling for false positives while…

Exploring Speaker Diarization with Mixture of Experts
Gaobin Yang, Maokui He, Shutong Niu, Ruoyu Wang, Hang Chen, Jun Du
https://arxiv.org/abs/2506.14750 h…

Exploring Speaker Diarization with Mixture of Experts
In this paper, we propose a novel neural speaker diarization system using memory-aware multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S), which integrates a memory-aware multi-speaker embedding module with a sequence-to-sequence architecture. The system leverages a memory module to enhance speaker embeddings and employs a Seq2Seq framework to efficiently map acoustic features to speaker labels. Additionally, we explore the application of mixture of experts in speaker dia…

discogs_label: Discogs label affiliations
Two bipartite networks of the affiliations between musical labels and either musical genres or musical "styles," as given in the discogs.com database. Edges represent that a label was involved in a production of a musical release of a given genre or given style. The date of this snapshot is uncertain.
This network has 270786 nodes and 4147665 edges.
Tags: Informational, Relatedness, Unweighted, Multigraph

Criteria-Based LLM Relevance Judgments
Naghmeh Farzi, Laura Dietz
https://arxiv.org/abs/2507.09488 https://arxiv.org/pdf/2507.09488…

Criteria-Based LLM Relevance Judgments
Relevance judgments are crucial for evaluating information retrieval systems, but traditional human-annotated labels are time-consuming and expensive. As a result, many researchers turn to automatic alternatives to accelerate method development. Among these, Large Language Models (LLMs) provide a scalable solution by generating relevance labels directly through prompting. However, prompting an LLM for a relevance label without constraints often results in not only incorrect predictions but also…

Self-reverse labelings of distance magic graphs
Petr Kov\'a\v{r}, Ksenija Rozman, Primo\v{z} \v{S}parl
https://arxiv.org/abs/2507.11226 https://…

Self-reverse labelings of distance magic graphs
A graph is distance magic if it admits a bijective labeling of its vertices by integers from $1$ up to the order of the graph in such a way that the sum of the labels of all the neighbors of a vertex is independent of a given vertex. We introduce the concept of a self-reverse distance magic labeling of a regular graph which allows for a more compact description of the graph and the labeling in terms of the corresponding quotient graph. We show that the members of several known infinite families…

NewsGuard is retiring "misinformation" and "disinformation" as primary labels, saying the words have become meaningless and politicized (McKenzie Sadeghi/NewsGuard's Reality Check)
https://www.newsguardrealitycheck.com/p/commentary-why-were-m…

Commentary: Why We’re Moving Beyond “Misinformation” and “Disinformation”
Welcome to Reality Check, a newsletter that helps you keep track of the false claims and online conspiracy theories that shape our world — and who’s behind them.

SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Andreas Lolos, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz, Aris Moustakas
https://arxiv.org/abs/2507.08711 …

SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available, without having access to instance-level annotations. This is usually the case in digital pathology, which consists of gigapixel sized images. While deterministic attention-based MIL approaches achieve strong bag-level performance, they often overlook the uncertainty inherent in instance relevance. In this paper, we address the lack of uncertainty quantification in instance-…

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

This https://arxiv.org/abs/2506.05047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Reliably detecting model failures in deployment without labels
The distribution of data changes over time; models operating operating in dynamic environments need retraining. But knowing when to retrain, without access to labels, is an open challenge since some, but not all shifts degrade model performance. This paper formalizes and addresses the problem of post-deployment deterioration (PDD) monitoring. We propose D3M, a practical and efficient monitoring algorithm based on the disagreement of predictive models, achieving low false positive rates under no…

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Yusdivia Molina-Rom\'an, David G\'omez-Ortiz, Ernestina Menasalvas-Ruiz, Jos\'e Gerardo Tamez-Pe\~na, Alejandro Santos-D\'iaz
https://arxiv.org/abs/2506.13964

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification a…

Stereotype graph: A mathematical framework of category stereotypes via graph theory
Yijia Yan
https://arxiv.org/abs/2506.12533 https://

Stereotype graph: A mathematical framework of category stereotypes via graph theory
In social psychology and cognitive science, there has been much interest in studying category stereotypes. However, we still lack a consensual mathematical definition or framework, which is necessary for us to hold a deeper understanding of stereotypes in human cognition. In this paper, we use graph theory to portray category stereotypes in human cognition, based on pairs of labels having special relations. By using methods and conclusions in graph theory (including algebraic graph theory and v…

discogs_label: Discogs label affiliations
Two bipartite networks of the affiliations between musical labels and either musical genres or musical "styles," as given in the discogs.com database. Edges represent that a label was involved in a production of a musical release of a given genre or given style. The date of this snapshot is uncertain.
This network has 270786 nodes and 4147665 edges.
Tags: Informational, Relatedness, Unweighted, Multigraph

Sources: UMG, Warner Music, and Sony Music are in talks to license their work to AI music services Udio and Suno and settle copyright infringement lawsuits (Lucas Shaw/Bloomberg)
https://www.bloomberg.com/news/articles/20

Exploring AR Label Placements in Visually Cluttered Scenarios
Ji Hwan Park, Braden Roper, Amirhossein Arezoumand, Tien Tran
https://arxiv.org/abs/2507.00198

Exploring AR Label Placements in Visually Cluttered Scenarios
We investigate methods for placing labels in AR environments that have visually cluttered scenes. As the number of items increases in a scene within the user' FOV, it is challenging to effectively place labels based on existing label placement guidelines. To address this issue, we implemented three label placement techniques for in-view objects for AR applications. We specifically target a scenario, where various items of different types are scattered within the user's field of view, and multip…

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Tianlong Ai, Tianzhu Liu, Haochen Jiang, Yanfeng Gu
https://arxiv.org/abs/2507.08741

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, existing deep learning-based methods face two major challenges: 1) They predominantly adopt a flat classification paradigm, which limits their ability to generate end-to-end multi-granularity hierarchical predictions aligned with tree-structured hierarchies used in practice. 2) Most cross-domain studies focus on perform…

Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Weicong Qin, Yi Xu, Weijie Yu, Teng Shi, Chenglei Shen, Ming He, Jianping Fan, Xiao Zhang, Jun Xu
https://arxiv.org/abs/2506.14437

Similarity = Value? Consultation Value Assessment and Alignment for Personalized Search
Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of 'value' labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for perso…

Distillation versus Contrastive Learning: How to Train Your Rerankers
Zhichao Xu, Zhiqi Huang, Shengyao Zhuang, Ashim Gupta, Vivek Srikumar
https://arxiv.org/abs/2507.08336

Distillation versus Contrastive Learning: How to Train Your Rerankers
Training text rerankers is crucial for information retrieval. Two primary strategies are widely used: contrastive learning (optimizing directly on ground-truth labels) and knowledge distillation (transferring knowledge from a larger reranker). While both have been studied in the literature, a clear comparison of their effectiveness for training cross-encoder rerankers under practical conditions is needed. This paper empirically compares these strategies by training rerankers of different size…

discogs_label: Discogs label affiliations
Two bipartite networks of the affiliations between musical labels and either musical genres or musical "styles," as given in the discogs.com database. Edges represent that a label was involved in a production of a musical release of a given genre or given style. The date of this snapshot is uncertain.
This network has 270786 nodes and 4147665 edges.
Tags: Informational, Relatedness, Unweighted, Multigraph

Independent labels and trade associations urge the EU to probe UMG's $775M acquisition of music services company Downtown, saying it gives UMG too much power (Daniel Thomas/Financial Times)
https://www.ft.com/content/1c6315ba-1369-41b2-9ea1-5c31077a0a50

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
Meng Zhang, Maosheng Xiang, Yuan-Sen Ting, Anish Maynur Amarsi, Hua-Wei Zhang, Jianrong Shi, Haibo Yuan, Haining Li, Jiahui Wang, Yaqian Wu, Tianmin Wu, Lanya Mou, Hong-liang Yan, Jifeng Liu
https://

Homogeneous Stellar Atmospheric Parameters and 22 Elemental Abundances for FGK Stars Derived From LAMOST Low-resolution Spectra with DD-Payne
A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated catalog of stellar labels derived from LAMOST low-resolution spectra in a physics-sensible and rigorous manner with DD-Payne, taking labels from high-resolution spectroscopy as training set. The catalog contains atmospheric parameters for 6.4 million stars re…

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
Hien Ohnaka, Yuma Shirahata, Byeongseon Park, Ryuichi Yamamoto
https://arxiv.org/abs/2506.04527

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
We propose a model to obtain phonemic and prosodic labels of speech that are coherent with graphemes. Unlike previous methods that simply fine-tune a pre-trained ASR model with the labels, the proposed model conditions the label generation on corresponding graphemes by two methods: 1) Add implicit grapheme conditioning through prompt encoder using pre-trained BERT features. 2) Explicitly prune the label hypotheses inconsistent with the grapheme during inference. These methods enable obtaining p…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Overview of the TREC 2022 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin, Ellen M. Voorhees, Ian Soboroff
https://arxiv.org/abs/2507.10865

Overview of the TREC 2022 deep learning track
This is the fourth year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels available for both passage and document ranking tasks. In addition, this year we also leverage both the refreshed passage and document collections that were released last year leading to a nearly $16$ times increase in the size of the passage collection and nearly four times increase in the document collection size. U…

Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Shuai Yuan, Shuang Chen, Tianwu Lin, Jie Wang, Peng Gong
https://arxiv.org/abs/2506.02574

Dynamic mapping from static labels: remote sensing dynamic sample generation with temporal-spectral embedding
Accurate remote sensing geographic mapping depends heavily on representative and timely sample data. However, rapid changes in land surface dynamics necessitate frequent updates, quickly rendering previously collected samples obsolete and imposing significant labor demands for continuous manual updates. In this study, we aim to address this problem by dynamic sample generation using existing single-date static labeled samples. We introduce TasGen, a two-stage automated framework to automaticall…

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
Shuai Wang, Zhenhua Liu, Jiaheng Wei, Xuanwu Yin, Dong Li, Emad Barsoum
https://arxiv.org/abs/2506.09532

Athena: Enhancing Multimodal Reasoning with Data-efficient Process Reward Models
We present Athena-PRM, a multimodal process reward model (PRM) designed to evaluate the reward score for each step in solving complex reasoning problems. Developing high-performance PRMs typically demands significant time and financial investment, primarily due to the necessity for step-level annotations of reasoning steps. Conventional automated labeling methods, such as Monte Carlo estimation, often produce noisy labels and incur substantial computational costs. To efficiently generate high-q…

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Hiroshi Matsuda, Chunpeng Ma, Masayuki Asahara
https://arxiv.org/abs/2506.09983

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Recent advances in large language models (LLMs) have enabled impressive performance in various tasks. However, standard prompting often struggles to produce structurally valid and accurate outputs, especially in dependency parsing. We propose a novel step-by-step instruction strategy, where universal part-of-speech tagging precedes the prediction of syntactic heads and dependency labels, and a simplified CoNLL-U like output format, our method achieves state-of-the-art accuracy on Universal Depe…

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

SCOTUS will review a dispute between Cox and major music labels after an appeals court threw out a $1B jury verdict against Cox over alleged piracy by customers (Blake Brittain/Reuters)
https://www.reuters.com/s…

Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Pinar Bisgin, Tom Strube, Niklas Tschorn, Michael Pantf\"order, Maximilian Fecke, Ingrid Ljungvall, Jens H\"aggstr\"om, Gerhard Wess, Christoph Schummer, Sven Meister, Falk M. Howar
https://arxiv.org/abs/2507.05950…

Improving AI-Based Canine Heart Disease Diagnosis with Expert-Consensus Auscultation Labeling
Noisy labels pose significant challenges for AI model training in veterinary medicine. This study examines expert assessment ambiguity in canine auscultation data, highlights the negative impact of label noise on classification performance, and introduces methods for label noise reduction. To evaluate whether label noise can be minimized by incorporating multiple expert opinions, a dataset of 140 heart sound recordings (HSR) was annotated regarding the intensity of holosystolic heart murmurs ca…

Overview of the TREC 2021 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin
https://arxiv.org/abs/2507.08191 https:/…

Overview of the TREC 2021 deep learning track
This is the third year of the TREC Deep Learning track. As in previous years, we leverage the MS MARCO datasets that made hundreds of thousands of human annotated training labels available for both passage and document ranking tasks. In addition, this year we refreshed both the document and the passage collections which also led to a nearly four times increase in the document collection size and nearly $16$ times increase in the size of the passage collection. Deep neural ranking models that em…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

R1-RE: Cross-Domain Relationship Extraction with RLVR
Runpeng Dai, Tong Zheng, Run Yang, Hongtu Zhu
https://arxiv.org/abs/2507.04642 https://

R1-RE: Cross-Domain Relationship Extraction with RLVR
Relationship extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labels-an approach that often suffers from poor out-of-domain (OOD) generalization. Inspired by the workflow of human annotators, we reframe RE as a reasoning task guided by annotation guidelines and introduce R1-RE, the first reinforcement learning with verifiable reward (RLVR) framework for RE tasks. Our method elic…

Mitigating Shortcut Learning with InterpoLated Learning
Michalis Korakakis, Andreas Vlachos, Adrian Weller
https://arxiv.org/abs/2507.05527 https://…

Mitigating Shortcut Learning with InterpoLated Learning
Empirical risk minimization (ERM) incentivizes models to exploit shortcuts, i.e., spurious correlations between input attributes and labels that are prevalent in the majority of the training data but unrelated to the task at hand. This reliance hinders generalization on minority examples, where such correlations do not hold. Existing shortcut mitigation approaches are model-specific, difficult to tune, computationally expensive, and fail to improve learned representations. To address these issu…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

Model-Driven Graph Contrastive Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
https://arxiv.org/abs/2506.06212 https://…

Model-Driven Graph Contrastive Learning
We propose $\textbf{MGCL}$, a model-driven graph contrastive learning (GCL) framework that leverages graphons (probabilistic generative models for graphs) to guide contrastive learning by accounting for the data's underlying generative process. GCL has emerged as a powerful self-supervised framework for learning expressive node or graph representations without relying on annotated labels, which are often scarce in real-world data. By contrasting augmented views of graph data, GCL has demonstrat…

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

malaria_genes: Malaria var DBLa HVR networks
Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant. Metadata includes two types of node labels, both based on sequence st…

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

discogs_label: Discogs label affiliations
Two bipartite networks of the affiliations between musical labels and either musical genres or musical "styles," as given in the discogs.com database. Edges represent that a label was involved in a production of a musical release of a given genre or given style. The date of this snapshot is uncertain.
This network has 270786 nodes and 4147665 edges.
Tags: Informational, Relatedness, Unweighted, Multigraph

discogs_label: Discogs label affiliations
Two bipartite networks of the affiliations between musical labels and either musical genres or musical "styles," as given in the discogs.com database. Edges represent that a label was involved in a production of a musical release of a given genre or given style. The date of this snapshot is uncertain.
This network has 270786 nodes and 4147665 edges.
Tags: Informational, Relatedness, Unweighted, Multigraph

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

discogs_label: Discogs label affiliations
Two bipartite networks of the affiliations between musical labels and either musical genres or musical "styles," as given in the discogs.com database. Edges represent that a label was involved in a production of a musical release of a given genre or given style. The date of this snapshot is uncertain.
This network has 270786 nodes and 4147665 edges.
Tags: Informational, Relatedness, Unweighted, Multigraph

terrorists_911: 9-11 terrorist network
Network of individuals and their known social associations, centered around the hijackers that carried out the September 11th, 2001 terrorist attacks. Associations extracted after-the-fact from public data. Metadata labels say which plane a person was on, if any, on 9/11.
This network has 62 nodes and 152 edges.
Tags: Social, Offline, Unweighted, Metadata

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

dbpedia_recordlabel: DBpedia artist-label affiliations
Bipartite networks of the affiliations (contractual relations) between artists and the record labels under which they have performed, as extracted from Wikipedia by the DBpedia project.
This network has 186758 nodes and 233286 edges.
Tags: Economic, Employment, Unweighted
http…

soc_net_comms: Networks with group metadata
Snapshots of LiveJournal, Friendster, Orkut, and YouTube online social networks, as well as DBLP and Amazon. Node metadata represents a post hoc definition of a 'community' that a node belongs to, derived from topical labels of the node or interest-based 'groups' that a node links to.
This network has 317080 nodes and 1049866 edges.
Tags: Online, Social, Collaboration, Informational, Relatedness, Unweighted, Metada…

soc_net_comms: Networks with group metadata
Snapshots of LiveJournal, Friendster, Orkut, and YouTube online social networks, as well as DBLP and Amazon. Node metadata represents a post hoc definition of a 'community' that a node belongs to, derived from topical labels of the node or interest-based 'groups' that a node links to.
This network has 317080 nodes and 1049866 edges.
Tags: Online, Social, Collaboration, Informational, Relatedness, Unweighted, Metada…