
2025-05-26 10:19:34
This https://arxiv.org/abs/2307.10434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csFL_…
This https://arxiv.org/abs/2307.10434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csFL_…
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
If you need automatic wrapping of labels in ggplot, the {ggtext} package by @clauswilke@genart.social has you covered: https://wilkelab.org/ggtext/articles/theme_elements.html But make sure you also check out the rest of the functionality of the package to add markdo…
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
Benedetta Muscato, Lucia Passaro, Gizem Gezici, Fosca Giannotti
https://arxiv.org/abs/2506.20209 …
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…
A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14655 https:…
Enhancing Homophily-Heterophily Separation: Relation-Aware Learning in Heterogeneous Graphs
Ziyu Zheng, Yaming Yang, Ziyu Guan, Wei Zhao, Weigang Lu
https://arxiv.org/abs/2506.20980
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?
Neurodiversity does not seem to encourage building on science but pointing out where it went wrong.
I can commend that but I was expecting more.
What I was expecting was the people that got all these labels to do more, to develop their own out-of-the-box theories about what they discovered.
Some did and called it "neurotypes" but it never got past the medical model.
#Neurodiversity
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…
Synthetic Data Matters: Re-training with Geo-typical Synthetic Labels for Building Detection
Shuang Song, Yang Tang, Rongjun Qin
https://arxiv.org/abs/2507.16657
You can use three.js as GIS to render contour lines from a Geotiff with customizable labels! Work in progress.
(P.S. can you tell where this might be? ;) ) #gischat
UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model
Yilong Hu, Shijie Chang, Lihe Zhang, Feng Tian, Weibing Sun, Huchuan Lu
https://arxiv.org/abs/2507.18362
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
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
es ist leichter eine Katze als Hund zu verkaufen, als Labels, Bands und Fanzines davon zu überzeugen ins Fediverse zu kommen. #frust
Tony Romo debates 'dynasty is over' labels for Chiefs, explains why Kansas City should worry opposition
https://www.cbssports.com/nfl/news/tony-ro
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
A Proof System with Causal Labels (Part I): checking Individual Fairness and Intersectionality
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14650
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
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
Bipartite graphs with minimum degree at least 15 are antimagic
Kecai Deng
https://arxiv.org/abs/2507.17302 https://arxiv.org/pdf/2507…
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
Probably Approximately Correct Labels
Emmanuel J. Cand\`es, Andrew Ilyas, Tijana Zrnic
https://arxiv.org/abs/2506.10908 https://arxiv…
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
Operator Forces For Coarse-Grained Molecular Dynamics
Leon Klein, Atharva Kelkar, Aleksander Durumeric, Yaoyi Chen, Frank No\'e
https://arxiv.org/abs/2506.19628
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://
An Analytical Neighborhood Enrichment Score for Spatial Omics
Axel Andersson, Hanna Nystr\"om
https://arxiv.org/abs/2506.18692 https://
Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction
Alice Barthe, Mahtab Yaghubi Rad, Michele Grossi, Vedran Dunjko
https://arxiv.org/abs/2506.17089 …
Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
Minsoo Kim, Jip Kim
https://arxiv.org/abs/2507.17194
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
This https://arxiv.org/abs/2504.11284 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…
ASR-Guided Speaker-Role Diarization and Diarization-Guided ASR Decoding
Arindam Ghosh, Mark Fuhs, Bongjun Kim, Anurag Chowdhury, Monika Woszczyna
https://arxiv.org/abs/2507.17765
AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking
Chunan Liu, Aurelien Pelissier, Yanjun Shao, Lilian Denzler, Andrew C. R. Martin, Brooks Paige, Mariia Rodriguez Martinez
https://arxiv.org/abs/2506.17857
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
"Switzerland Rolls Out Labels Flagging Animal Suffering In Food Products"
#Switzerland #Animals #Food
@… The button labels seem to be tactual, like one can identify the numbers by touching them. The labels don't. If one can't see well, that UX problem gets even harder.
Obscured but Not Erased: Evaluating Nationality Bias in LLMs via Name-Based Bias Benchmarks
Giulio Pelosio, Devesh Batra, No\'emie Bovey, Robert Hankache, Cristovao Iglesias, Greig Cowan, Raad Khraishi
https://arxiv.org/abs/2507.16989
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
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…
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…
Near-Optimal Vertex Fault-Tolerant Labels for Steiner Connectivity
Koustav Bhanja, Asaf Petruschka
https://arxiv.org/abs/2506.23215 https://
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
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
« Exemples de labels (#GitLab) de gestion de projet »
https://notes.sklein.xyz/2025-05-13_0938/zen/
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
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
Flexible and Efficient Drift Detection without Labels
Nelvin Tan, Yu-Ching Shih, Dong Yang, Amol Salunkhe
https://arxiv.org/abs/2506.08734 https://
This https://arxiv.org/abs/2506.02763 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…
Dvorak-Dell-Grohe-Rattan theorem via an asymptotic argument
Alexander Kozachinskiy
https://arxiv.org/abs/2507.14669 https://arxiv.org…
Semi-supervised Community Detection using Glauber Dynamics for an Ising Model
Konstantin Avrachenkov, Diego Goldsztajn
https://arxiv.org/abs/2506.09223 htt…
This https://arxiv.org/abs/2506.05047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…
Label Unification for Cross-Dataset Generalization in Cybersecurity NER
Maciej Jalocha, Johan Hausted Schmidt, William Michelseen
https://arxiv.org/abs/2507.13870
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
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…
Attributes Shape the Embedding Space of Face Recognition Models
Pierrick Leroy, Antonio Mastropietro, Marco Nurisso, Francesco Vaccarino
https://arxiv.org/abs/2507.11372
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
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
Criteria-Based LLM Relevance Judgments
Naghmeh Farzi, Laura Dietz
https://arxiv.org/abs/2507.09488 https://arxiv.org/pdf/2507.09488…
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 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…
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
NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
Dzung Dinh, Boqi Chen, Marc Niethammer, Junier Oliva
https://arxiv.org/abs/2507.12412
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
SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Andreas Lolos, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz, Aris Moustakas
https://arxiv.org/abs/2507.08711 …
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://…
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
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…
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
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
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
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
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
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
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
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…
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…
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
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
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…
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
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…
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…
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