Tootfinder

wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
Kate Barnes, Mia Ellis-Einhorn, Carolina Ch\'avez-Ruelas, Nayera Hasan, Mohammad Fanous, Blair D. Sullivan, Sorelle Friedler, Aaron Clauset
https://arxiv.org/abs/2506.04435…

Edge interventions can mitigate demographic and prestige disparities in the Computer Science coauthorship network
Social factors such as demographic traits and institutional prestige structure the creation and dissemination of ideas in academic publishing. One place these effects can be observed is in how central or peripheral a researcher is in the coauthorship network. Here we investigate inequities in network centrality in a hand-collected data set of 5,670 U.S.-based faculty employed in Ph.D.-granting Computer Science departments and their DBLP coauthorship connections. We introduce algorithms for comb…

Music-induced cognitive change and whole-brain network flexibility: a pilot study https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1567605/full Now what might visual-to-auditory sensory substitution train…

Frontiers | Music-induced cognitive change and whole-brain network flexibility: a pilot study
IntroductionCognitive impairment that exceeds age-related cognitive decline is a risk factor for Alzheimer’s disease and related dementias. As the older adul...

The "Gollum effect" - wow didn't know this name, but yes, this is exactly what makes certain fields so terrible (like network science). It's just about old men behaving like Gorillas 🤮 https://www.nature.com/articles/d41586-025-01588-w
It's so n…

Science’s ‘Gollum effect’: PhDs bear brunt of territorial behaviour
Survey respondents at all career stages report colleagues engaging in territorial and possessive behaviours — but early-career researchers are most often affected.

netscience: Scientific collaborations in network science (2006)
A coauthorship network among scientists working on network science, from 2006. This network is a one-mode projection from the bipartite graph of authors and their scientific publications.
This network has 1589 nodes and 2742 edges.
Tags: Social, Collaboration, Weighted, Projection

netscience: Scientific collaborations in network science (2006)
A coauthorship network among scientists working on network science, from 2006. This network is a one-mode projection from the bipartite graph of authors and their scientific publications.
This network has 1589 nodes and 2742 edges.
Tags: Social, Collaboration, Weighted, Projection

China’s quantum satellite can be hacked, Singapore-based scientist warns
https://www.scmp.com/news/china/science/article/3312399/chinas-quantum-satellite-can-be-hacked-sing…

China’s quantum satellite can be hacked, Singapore-based scientist warns
Hackers could intercept coded messages sent by lasers on China’s quantum communication network, research suggests.

This https://arxiv.org/abs/2307.14984 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanation, and policy-making support, among others. In this work, we harness the formidable human-like capabilities exhibited by large language models (LLMs) in sensing, reasoning, and behaving, and utilize these qualities to construct the S$^3$ system (short for $\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$y…

cs_department: Aarhus Computer Science department relationships
Multiplex network consisting of 5 edge types corresponding to online and offline relationships (Facebook, leisure, work, co-authorship, lunch) between employees of the Computer Science department at Aarhus. Data hosted by Manlio De Domenico.
This network has 61 nodes and 620 edges.
Tags: Social, Relationships, Multilayer, Unweighted

cs_department: Aarhus Computer Science department relationships
Multiplex network consisting of 5 edge types corresponding to online and offline relationships (Facebook, leisure, work, co-authorship, lunch) between employees of the Computer Science department at Aarhus. Data hosted by Manlio De Domenico.
This network has 61 nodes and 620 edges.
Tags: Social, Relationships, Multilayer, Unweighted

A generative modeling / Physics-Informed Neural Network approach to random differential equations
Georgios Arampatzis, Stylianos Katsarakis, Charalambos Makridakis
https://arxiv.org/abs/2507.01687

A generative modeling / Physics-Informed Neural Network approach to random differential equations
The integration of Scientific Machine Learning (SciML) techniques with uncertainty quantification (UQ) represents a rapidly evolving frontier in computational science. This work advances Physics-Informed Neural Networks (PINNs) by incorporating probabilistic frameworks to effectively model uncertainty in complex systems. Our approach enhances the representation of uncertainty in forward problems by combining generative modeling techniques with PINNs. This integration enables in a systematic fas…

This https://arxiv.org/abs/2501.07327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Community Aware Temporal Network Generation
The advantages of temporal networks in capturing complex dynamics, such as diffusion and contagion, has led to breakthroughs in real world systems across numerous fields. In the case of human behavior, face-to-face interaction networks enable us to understand the dynamics of how communities emerge and evolve in time through the interactions, which is crucial in fields like epidemics, sociological studies and urban science. However, state-of-the-art datasets suffer from a number of drawbacks, su…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Nitesh Kumar (Department of Physics, Applied Science Cluster, University of Petroleum and Energy Studies), Harinder P. Singh (Department of Physics and Astrophysics, University of Delhi, Delhi, India), Oleg Malkov (Institute of Astronomy of the Russian Academy of Sciences), Santosh Joshi (Aryabhatta Research Institute of Observational Sciences), Kefeng Tan (National Astronomical Observatories, C…

Extraction of Physical Parameters of RRab Variables using Neural Network based Interpolator
Determining the physical parameters of pulsating variable stars such as RR Lyrae is essential for understanding their internal structure, pulsation mechanisms, and evolutionary state. In this study, we present a machine learning framework that uses feedforward artificial neural networks (ANNs) to infer stellar parameters-mass ($M$), luminosity (log($L/L_\odot$)), effective temperature (log($T_{\rm eff}$)), and metallicity ($Z$)-directly from Transiting Exoplanet Survey Satellite (TESS) light cu…

netscience: Scientific collaborations in network science (2006)
A coauthorship network among scientists working on network science, from 2006. This network is a one-mode projection from the bipartite graph of authors and their scientific publications.
This network has 1589 nodes and 2742 edges.
Tags: Social, Collaboration, Weighted, Projection

System-performance and cost modeling of Large Language Model training and inference
Wenzhe Guo, Joyjit Kundu, Uras Tos, Weijiang Kong, Giuliano Sisto, Timon Evenblij, Manu Perumkunnil
https://arxiv.org/abs/2507.02456

System-performance and cost modeling of Large Language Model training and inference
Large language models (LLMs), based on transformer architectures, have revolutionized numerous domains within artificial intelligence, science, and engineering due to their exceptional scalability and adaptability. However, the exponential growth in LLM size and complexity has outpaced advancements in compute capacity, memory bandwidth, network performance, and cost efficiency, posing significant challenges to their scalability on distributed systems. To address these limitations, alternative m…

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Annamaria Defilippo, Ugo Lomoio, Barbara Puccio, Pierangelo Veltri, Pietro Hiram Guzzi
https://arxiv.org/abs/2506.04271

ExDiff: A Framework for Simulating Diffusion Processes on Complex Networks with Explainable AI Integration
Understanding and controlling diffusion processes in complex networks is critical across domains ranging from epidemiology to information science. Here, we present ExDiff, an interactive and modular computational framework that integrates network simulation, graph neural networks (GNNs), and explainable artificial intelligence (XAI) to model and interpret diffusion dynamics. ExDiff combines classical compartmental models with deep learning techniques to capture both the structural and temporal …

webkb: WebKB graphs (1998)
Web graphs crawled from four Computer Science departments in 1998, with each page manually classified into one of 7 categories: course, department, faculty, project, staff, student, or other. All graphs included in a single .zip; also included are 'co-citation' graphs, which links i and j if they both point to some k. Edge weights count the number of links from i to j.
This network has 349 nodes and 1393 edges.
Tags: Informational, Web gra…

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Rafael Ferreira da Silva, Milad Abolhasani, Dionysios A. Antonopoulos, Laura Biven, Ryan Coffee, Ian T. Foster, Leslie Hamilton, Shantenu Jha, Theresa Mayer, Benjamin Mintz, Robert G. Moore, Salahudin Nimer, Noah Paulson, Woong Shin, Frederic Suter, Mitra Taheri, Michela Taufer, Newell R. Washburn

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institu…

FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Wenqi Jia, Ying Huang, Jian Xu, Zhewen Hu, Sian Jin, Jiannan Tian, Yuede Ji, Miao Yin
https://arxiv.org/abs/2507.01224

FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often reach petabyte to exabyte scales. However, managing these vast data volumes introduces significant I/O and network bottlenecks, limiting practical performance and scalability. While cutting-edge lossy compression frameworks powered by deep neural networks (DNNs) …

Robust design of bicycle infrastructure networks
https://www.nature.com/articles/s41598-025-99976-9

Robust design of bicycle infrastructure networks - Scientific Reports
Promoting active mobility like cycling relies on the availability of well-connected, high-quality bicycle networks. However, expanding these networks over an extended planning horizon presents one of the most complex challenges in transport science. This complexity arises from the intricate interactions between infrastructure availability and usage, such as network spillover effects and mode choice substitutions. In this paper, we approach the problem from two perspectives: direct optimization …

This https://arxiv.org/abs/2409.15683 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum DeepONet: Neural operators accelerated by quantum computing
In the realm of computational science and engineering, constructing models that reflect real-world phenomena requires solving partial differential equations (PDEs) with different conditions. Recent advancements in neural operators, such as deep operator network (DeepONet), which learn mappings between infinite-dimensional function spaces, promise efficient computation of PDE solutions for a new condition in a single forward pass. However, classical DeepONet entails quadratic complexity concerni…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 40421 nodes and 175692 edges.
Tags: Social, Collaborati…

wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
Amro M. Farid, Amirreza Hosseini, John C. Little
https://arxiv.org/abs/2505.24046

A Conceptual Introduction to Hetero-functional Graph Theory for Systems-of-Systems
A defining feature of twenty first century engineering challenges is their inherent complexity, demanding the convergence of knowledge across diverse disciplines. Establishing consistent methodological foundations for engineering systems remains a challenge -- one that both systems engineering and network science have sought to address. Model-based systems engineering (MBSE) has recently emerged as a practical, interdisciplinary approach for developing complex systems from concept through imple…

netscience: Scientific collaborations in network science (2006)
A coauthorship network among scientists working on network science, from 2006. This network is a one-mode projection from the bipartite graph of authors and their scientific publications.
This network has 1589 nodes and 2742 edges.
Tags: Social, Collaboration, Weighted, Projection

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- A Certified Proof Checker for Deep Neural Network Verification in Imandra
Remi Desmartin, Omri Isac, Grant Passmore, Ekaterina Komendantskaya, Kathrin Stark, Guy Katz

Rethinking Scientific Rankings with Breakthrough and Disruption Metrics: A Complex Network-Based Approach
Adarsh Raghuvanshi, Hrishidev Unni, Vinayak, Anirban Chakraborti
https://arxiv.org/abs/2506.18804

Rethinking Scientific Rankings with Breakthrough and Disruption Metrics: A Complex Network-Based Approach
Scientific progress is often driven by groundbreaking and disruptive research, yet traditional metrics for evaluating contributions to science often emphasize quantity over quality. In this study, we consider metrics that focus on the quality of scientific performance of countries and propose a novel framework that facilitates a competitive ranking of these countries based on their performance, highlighting the impact of their contributions. Using a bipartite network and centrality measures com…

netscience: Scientific collaborations in network science (2006)
A coauthorship network among scientists working on network science, from 2006. This network is a one-mode projection from the bipartite graph of authors and their scientific publications.
This network has 1589 nodes and 2742 edges.
Tags: Social, Collaboration, Weighted, Projection

This https://arxiv.org/abs/2304.05775 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Mapping compact memory-biased dynamics reveals overlapping communities
Researchers use networks to model relational data from complex systems and apply tools from network science to reveal their organisation and function. Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, identifying such overlapping flow-based communities typically requires higher-order relational data, which is often unavailable. To address this ch…

A 3D model simulation of hydrogen chloride photochemistry on Mars: Comparison with satellite data
Benjamin Benne (The University of Edinburgh, School of GeoSciences, UK, Centre for Exoplanet Science, University of Edinburgh, UK), Paul I. Palmer (The University of Edinburgh, School of GeoSciences, UK, Centre for Exoplanet Science, University of Edinburgh, UK), Benjamin M. Taysum (DLR, Germany), Kevin S. Olsen (Department of Physics, University of Oxford, UK, School of Physical Sciences,…

A 3D model simulation of hydrogen chloride photochemistry on Mars: Comparison with satellite data
HCl was detected in the Martian atmosphere by the NOMAD and ACS spectrometers aboard the ExoMars TGO. Photochemical models show that using gas-phase chemistry alone is insufficient to reproduce these data. Recent work has developed a heterogeneous chemical network within a 1D photochemistry model, guided by the seasonal variability in HCl. The aim of this work is to show that incorporating heterogeneous chlorine chemistry into a global 3D model of Martian photochemistry with conventional gas-ph…

Gender Differences in International Research Collaboration in European Union
Elsa Fontainha, Tanya Ara\'ujo
https://arxiv.org/abs/2507.00619 https://…

Gender Differences in International Research Collaboration in European Union
This paper investigates International Research Collaboration (IRC) among European Union (EU) countries from 2011 to 2022, with emphasis on gender-based authorship patterns. Drawing from the Web of Science Social Science Citation Index (WoS-SSCI) database, a large dataset of IRC articles was constructed, annotated with categories of authorship based on gender, author affiliation, and COVID-19 subject as topic. Using network science, the study maps collaboration structures and reveals gendered di…

Bayesian community detection in assortative stochastic block model with unknown number of communities
Martina Amongero, Pierpaolo De Blasi
https://arxiv.org/abs/2506.19576

Bayesian community detection in assortative stochastic block model with unknown number of communities
Structured data in the form of networks is increasingly common in a number of fields, including social sciences, biology, physics, computer science, and many others. A key task in network analysis is community detection, which typically consists of dividing the nodes into groups such that nodes within a group are strongly connected, while connections between groups are relatively scarcer. A generative model well-suited for the formation of such communities is the assortative stochastic block mo…

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Ryan Barker
https://arxiv.org/abs/2506.09878 https://

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Virtualising the Radio Access Network (RAN) is widely touted as the corner-stone of affordable 5G and a prerequisite for AI-native 6G. Yet current discourse often isolates spectrum policy, cloud engineering and organisational readiness into silos. This paper delivers an integrated analysis that spans science, technology, business strategy and culture. I first review spectrum-auction economics and show-via a comparative study of T-Mobile US and Verizon-that mid-band contiguity leveraged through …

Directed Acyclic Graph Convolutional Networks
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
https://arxiv.org/abs/2506.12218 https://arxi…

Directed Acyclic Graph Convolutional Networks
Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduce the DAG Convolutional Network (DCN), a novel graph neural network (GNN) architecture designed specifically for convolutional learning from signals supported on DAGs. The DCN leverages causal graph filters to learn nodal representations that account for the partial ordering inherent to DAGs, a strong inductive bias d…

Replaced article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[2/2]:
- Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dy...
Naoki Matsumura, Yuta Yoshimoto, Yuto Iwasaki, Meguru Ya…

dblp_coauthor_snap: DBLP authors (2012)
A coauthorship network extracted from the DBLP computer science manuscript database, in 2012. This network is a one-mode projection from the bipartite graph of computer scientists and their publications.
This network has 425957 nodes and 1049866 edges.
Tags: Social, Collaboration, Unweighted, Metadata, Projection

From Static to Dynamic: Exploring Temporal Networks in Systems Biology
Abir Khazaal, Fatemeh Vafaee
https://arxiv.org/abs/2505.10741 https://

From Static to Dynamic: Exploring Temporal Networks in Systems Biology
Network science has become an essential interdisciplinary tool for understanding complex biological systems. However, because these systems undergo continuous, often stimulus-driven changes in both structure and function, traditional static network approaches frequently fall short in capturing their dynamic nature. Dynamic network analysis (DNA) addresses this limitation and offers a powerful framework to investigate these evolving relationships. This work focuses on temporal networks, a centra…

Generating Large Semi-Synthetic Graphs of Any Size
Rodrigo Tuna, Carlos Soares
https://arxiv.org/abs/2507.02166 https://arxiv.org/pdf…

Generating Large Semi-Synthetic Graphs of Any Size
Graph generation is an important area in network science. Traditional approaches focus on replicating specific properties of real-world graphs, such as small diameters or power-law degree distributions. Recent advancements in deep learning, particularly with Graph Neural Networks, have enabled data-driven methods to learn and generate graphs without relying on predefined structural properties. Despite these advances, current models are limited by their reliance on node IDs, which restricts thei…

This https://arxiv.org/abs/2102.00055 has been replaced.
link: https://scholar.google.com/scholar?q=a

Lower Bounds on Information Requirements for Causal Network Inference
Recovery of the causal structure of dynamic networks from noisy measurements has long been a problem of interest across many areas of science and engineering. Many algorithms have been proposed, but there is little work that compares the performance of the algorithms to converse bounds in a non-asymptotic setting. As a step to address this problem, this paper gives lower bounds on the error probability for causal network support recovery in a linear Gaussian setting. The bounds are based on Mon…

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian, Shuiwang Ji
https://arxiv.org/abs/2506.09398

Efficient Prediction of SO(3)-Equivariant Hamiltonian Matrices via SO(2) Local Frames
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials science. Motivated by the inherent relationship between the off-diagonal blocks of the Hamiltonian matrix and the SO(2) local frame, we propose a novel and efficient network, called QHNetV2, that achieves global SO(3) equivariance without the costly SO(3) Clebsch-Gordan tensor products. This is achieved by introducing a set o…

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Thomas King, Sergio Vinciguerra
https://arxiv.org/abs/2505.21312 https://

Generalised Time-Series Analysis of Fault Mechanics Using Explainable AI
Understanding how faults nucleate and grow is a critical problem in earthquake science and hazard assessment. This study examines fault development in Alzo granite under triaxial pressures ranging from 5 to 40 MPa by applying a Time Delay Neural Network (TDNN) to multi-parameter acoustic emission (AE) data. The TDNN integrates waveform-derived attributes, including peak delay and scattering attenuation, with occurrence-based metrics such as time distributions, Gutenberg-Richter b-values, and sp…

dblp_coauthor_snap: DBLP authors (2012)
A coauthorship network extracted from the DBLP computer science manuscript database, in 2012. This network is a one-mode projection from the bipartite graph of computer scientists and their publications.
This network has 425957 nodes and 1049866 edges.
Tags: Social, Collaboration, Unweighted, Metadata, Projection

Investigating Resiliency of Transportation Network Under Targeted and Potential Climate Change Disruptions
Maedeh Rahimitouranposhti, Bharat Sharma, Mustafa Can Camur, Olufemi A. Omitaomu, Xueping Li
https://arxiv.org/abs/2506.19102

Investigating Resiliency of Transportation Network Under Targeted and Potential Climate Change Disruptions
Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models th…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Ryan DeWolfe, Fran\c{c}ois Th\'eberge
https://arxiv.org/abs/2506.03105 ht…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph…

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted

Our chapter on Urban #mobility is finally published, in the great Compendium of Urban Complexity!
https://link.springer.com/chapter/10.1007/978-3-031-82666-5_4…

Urban Mobility
In this chapter, we discuss urban mobility from a complexity science perspective. First, we give an overview of the datasets that enable this approach, such as mobile phone records, location-based social network traces, or GPS trajectories from sensors installed on...

Triadic Novelty: A Typology and Measurement Framework for Recognizing Novel Contributions in Science
Jin Ai, Richard S. Steinberg, Chao Guo, Filipi Nascimento Silva
https://arxiv.org/abs/2506.17851

Triadic Novelty: A Typology and Measurement Framework for Recognizing Novel Contributions in Science
Scientific progress depends on novel ideas, but current reward systems often fail to recognize them. Many existing metrics conflate novelty with popularity, privileging ideas that fit existing paradigms over those that challenge them. This study develops a theory-driven framework to better understand how different types of novelty emerge, take hold, and receive recognition. Drawing on network science and theories of discovery, we introduce a triadic typology: Pioneers, who introduce entirely ne…

Network evolution analysis: a usage-centric approach applied on cycling infrastructure
https://josis.org/index.php/josis/article/view/379

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective
Josef Hoppe, Vincent P. Grande, Michael T. Schaub
https://arxiv.org/abs/2506.09726

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective
Cell complexes (CCs) are a higher-order network model deeply rooted in algebraic topology that has gained interest in signal processing and network science recently. However, while the processing of signals supported on CCs can be described in terms of easily-accessible algebraic or combinatorial notions, the commonly presented definition of CCs is grounded in abstract concepts from topology and remains disconnected from the signal processing methods developed for CCs. In this paper, we aim to …

This https://arxiv.org/abs/2505.18300 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\boldsymbolμ$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on pa…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 31163 nodes and 120029 edges.
Tags: Social, Collaborati…

netscience: Scientific collaborations in network science (2006)
A coauthorship network among scientists working on network science, from 2006. This network is a one-mode projection from the bipartite graph of authors and their scientific publications.
This network has 1589 nodes and 2742 edges.
Tags: Social, Collaboration, Weighted, Projection

SocioXplorer: An Interactive Tool for Topic and Network Analysis in Social Data
Sandrine Chausson, Youssef Al Hariri, Walid Magdy, Bj\"orn Ross
https://arxiv.org/abs/2506.18845

SocioXplorer: An Interactive Tool for Topic and Network Analysis in Social Data
SocioXplorer is a powerful interactive tool that computational social science researchers can use to understand topics and networks in social data from Twitter (X) and YouTube. It integrates, among other things, artificial intelligence, natural language processing and social network analysis. It can be used with ``live" datasets that receive regular updates. SocioXplorer is an extension of a previous system called TwiXplorer, which was limited to the analysis of archival Twitter (X) data. Socio…

webkb: WebKB graphs (1998)
Web graphs crawled from four Computer Science departments in 1998, with each page manually classified into one of 7 categories: course, department, faculty, project, staff, student, or other. All graphs included in a single .zip; also included are 'co-citation' graphs, which links i and j if they both point to some k. Edge weights count the number of links from i to j.
This network has 300 nodes and 1155 edges.
Tags: Informational, Web gra…

faculty_hiring_us: Faculty hiring networks in the US (2022)
Networks of faculty hiring for all PhD-granting US universities over the decade 2011–2020. Each node is a PhD-granting institution, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2020). This dataset is divided into separate networks for all 107 fields, as well as aggregate networks for 8 domains, and an overall network for …

This https://arxiv.org/abs/2503.17945 has been replaced.
initial toot: https://mastoxiv.page/@a…

Deep Learning Assisted Denoising of Experimental Micrographs
Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep learning approach for robust microstructure image denoising, combining phase-field simulations, Fourier transform techniques, and an attention-based neural network. The innovative framework addresses dataset limitations by synthetically generating training data by combining computational phase-field microstructures with…

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
Kiran Sharmaa, Parul Khurana, Ziya Uddina
https://arxiv.org/abs/2506.09056 https://

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis
The exponential increase in academic publications has made it increasingly difficult for researchers to remain up to date and systematically synthesize knowledge scattered across vast and fragmented research domains. Literature reviews, particularly those supported by bibliometric methods, have become essential in organizing prior findings and guiding future research directions. While numerous tools exist for bibliometric analysis and network science, there is currently no single platform that …

wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

webkb: WebKB graphs (1998)
Web graphs crawled from four Computer Science departments in 1998, with each page manually classified into one of 7 categories: course, department, faculty, project, staff, student, or other. All graphs included in a single .zip; also included are 'co-citation' graphs, which links i and j if they both point to some k. Edge weights count the number of links from i to j.
This network has 300 nodes and 1155 edges.
Tags: Informational, Web gra…

A Benchmarking Framework for Network Classification Methods
Joao V. Merenda, Gonzalo Travieso, Odemir M. Bruno
https://arxiv.org/abs/2506.06513 https://

A Benchmarking Framework for Network Classification Methods
Network classification plays a crucial role in the study of complex systems, impacting fields like biology, sociology, and computer science. In this research, we present an innovative benchmark dataset made up of synthetic networks that are categorized into various classes and subclasses. This dataset is specifically crafted to test the effectiveness and resilience of different network classification methods. To put these methods to the test, we also introduce various types and levels of struct…

wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

wiki_science: Wikipedia Map of Science (2020)
A network of scientific fields, extracted from the English Wikipedia in early 2020. Nodes are wikipedia pages representing natural, formal, social and applied sciences, and two nodes are linked if the cosine similarity of the page content is above a threshold. See <http://www.s…

dblp_coauthor_snap: DBLP authors (2012)
A coauthorship network extracted from the DBLP computer science manuscript database, in 2012. This network is a one-mode projection from the bipartite graph of computer scientists and their publications.
This network has 425957 nodes and 1049866 edges.
Tags: Social, Collaboration, Unweighted, Metadata, Projection

This https://arxiv.org/abs/2402.16887 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Comprehensive Survey on Artificial Intelligence for Complex Network: Potential, Methodology and Application
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world n…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 8361 nodes and 15751 edges.
Tags: Social, Collaboration…

dblp_cite: DBLP citations (2014)
Citations among papers contained in the DBLP computer science bibliography. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. This snapshot from May 2014.
This network has 12590 nodes and 49759 edges.
Tags: Informational, Citation, Unweighted

webkb: WebKB graphs (1998)
Web graphs crawled from four Computer Science departments in 1998, with each page manually classified into one of 7 categories: course, department, faculty, project, staff, student, or other. All graphs included in a single .zip; also included are 'co-citation' graphs, which links i and j if they both point to some k. Edge weights count the number of links from i to j.
This network has 433 nodes and 1941 edges.
Tags: Informational, Web gra…

webkb: WebKB graphs (1998)
Web graphs crawled from four Computer Science departments in 1998, with each page manually classified into one of 7 categories: course, department, faculty, project, staff, student, or other. All graphs included in a single .zip; also included are 'co-citation' graphs, which links i and j if they both point to some k. Edge weights count the number of links from i to j.
This network has 334 nodes and 32988 edges.
Tags: Informational, Web gr…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 8361 nodes and 15751 edges.
Tags: Social, Collaboration…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 16706 nodes and 121251 edges.
Tags: Social, Collaborati…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 31163 nodes and 120029 edges.
Tags: Social, Collaborati…

arxiv_collab: Scientific collaborations in physics (1995-2005)
Collaboration graphs for scientists, extracted from the Los Alamos e-Print arXiv (physics), for 1995-1999 for three categories, and additionally for 1995-2003 and 1995-2005 for one category. For copyright reasons, the MEDLINE (biomedical research) and NCSTRL (computer science) collaboration graphs from this paper are not publicly available.
This network has 16726 nodes and 47594 edges.
Tags: Social, Collaboratio…

cora: CORA citations (1998)
Citations among papers indexed by CORA, from 1998, an early computer science research paper search engine. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. The dates of these snapshots are uncertain.
This network has 23166 nodes and 91500 edges.
Tags: Informational, Citation, Unweighted