Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Quantifying metadata relevance to network block structure using description length (2311.18705v4)

Published 30 Nov 2023 in cs.SI

Abstract: Network analysis is often enriched by including an examination of node metadata. In the context of understanding the mesoscale of networks it is often assumed that node groups based on metadata and node groups based on connectivity patterns are intrinsically linked. This assumption is increasingly being challenged, whereby metadata might be entirely unrelated to structure or, similarly, multiple sets of metadata might be relevant to the structure of a network in different ways. We propose the metablox tool to quantify the relationship between a network's node metadata and its mesoscale structure, measuring the strength of the relationship and the type of structural arrangement exhibited by the metadata. We show on a number of synthetic and empirical networks that our tool distinguishes relevant metadata and allows for this in a comparative setting, demonstrating that it can be used as part of systematic meta analyses for the comparison of networks from different domains.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (46)
  1. Francois Lorrain and Harrison C White “Structural equivalence of individuals in social networks” In The Journal of mathematical sociology 1.1 Taylor & Francis, 1971, pp. 49–80
  2. Harrison C White, Scott A Boorman and Ronald L Breiger “Social structure from multiple networks. I. Blockmodels of roles and positions” In American journal of sociology 81.4 University of Chicago Press, 1976, pp. 730–780
  3. Paul W. Holland, Kathryn Blackmond Laskey and Samuel Leinhardt “Stochastic Blockmodels: First Steps” In Social Networks 5.2, 1983, pp. 109–137 DOI: 10.1016/0378-8733(83)90021-7
  4. “Stochastic Blockmodels and Community Structure in Networks” In Physical Review E 83.1, 2011, pp. 016107 DOI: 10.1103/PhysRevE.83.016107
  5. Tiago P. Peixoto “Nonparametric Bayesian Inference of the Microcanonical Stochastic Block Model” In Physical Review E 95.1, 2017, pp. 012317 DOI: 10.1103/PhysRevE.95.012317
  6. Tiago P Peixoto “Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models” In Physical Review E 89.1 APS, 2014, pp. 012804
  7. Mark EJ Newman and Aaron Clauset “Structure and Inference in Annotated Networks” In Nature communications 7.1 Nature Publishing Group, 2016, pp. 1–11
  8. Darko Hric, Tiago P. Peixoto and Santo Fortunato “Network Structure, Metadata, and the Prediction of Missing Nodes and Annotations” In Physical Review X 6.3, 2016, pp. 031038 DOI: 10.1103/PhysRevX.6.031038
  9. Leto Peel, Daniel B Larremore and Aaron Clauset “The Ground Truth about Metadata and Community Detection in Networks” In Science Advances 3.5, 2017, pp. e1602548 DOI: 10.1126/sciadv.1602548
  10. Lada A Adamic and Natalie Glance “The political blogosphere and the 2004 US election: divided they blog” In Proceedings of the 3rd international workshop on Link discovery, 2005, pp. 36–43
  11. “Political Polarization on Twitter” In Proceedings of the International Aaai Conference on Web and Social Media 5, 2011, pp. 89–96
  12. “Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?” In Psychological Science 26.10 SAGE Publications Inc, 2015, pp. 1531–1542 DOI: 10.1177/0956797615594620
  13. Henry Small and Belver C Griffith “The structure of scientific literatures I: Identifying and graphing specialties” In Science studies 4.1 Sage Publications Sage CA: Thousand Oaks, CA, 1974, pp. 17–40
  14. Wayne W. Zachary “An Information Flow Model for Conflict and Fission in Small Groups” In Journal of Anthropological Research 33.4, 1977, pp. 452–473 DOI: 10.1086/jar.33.4.3629752
  15. Amanda L Traud, Peter J Mucha and Mason A Porter “Social structure of facebook networks” In Physica A: Statistical Mechanics and its Applications 391.16 Elsevier, 2012, pp. 4165–4180
  16. “Defining and evaluating network communities based on ground-truth” In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, 2012, pp. 1–8
  17. “Computer science fields as ground-truth communities: Their impact, rise and fall” In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013, pp. 426–433
  18. Jaewon Yang, Julian McAuley and Jure Leskovec “Community Detection in Networks with Node Attributes” In 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1151–1156 DOI: 10.1109/ICDM.2013.167
  19. “Clustering Attributed Graphs: Models, Measures and Methods” In Network Science 3.3 Cambridge University Press, 2015, pp. 408–444 DOI: 10.1017/nws.2015.9
  20. Norbert Binkiewicz, Joshua T Vogelstein and Karl Rohe “Covariate-assisted spectral clustering” In Biometrika 104.2 Oxford University Press, 2017, pp. 361–377
  21. Darko Hric, Richard K Darst and Santo Fortunato “Community detection in networks: Structural communities versus ground truth” In Physical Review E 90.6 APS, 2014, pp. 062805
  22. Tiago P. Peixoto “The graph-tool python library” In figshare, 2014 DOI: 10.6084/m9.figshare.1164194
  23. Tiago P. Peixoto “Revealing Consensus and Dissensus between Network Partitions” In Physical Review X 11.2, 2021, pp. 021003 DOI: 10.1103/PhysRevX.11.021003
  24. “Generative Models for Two-Ground-Truth Partitions in Networks” In Physical Review E 108.5 American Physical Society, 2023, pp. 054308 DOI: 10.1103/PhysRevE.108.054308
  25. Natalie Stanley, Marc Niethammer and Peter J. Mucha “Testing Alignment of Node Attributes with Network Structure Through Label Propagation”, 2018 arXiv:1805.07375 [cs.SI]
  26. Peter D Grünwald “The minimum description length principle” MIT press, 2007
  27. Krzysztof Nowicki and Tom A B Snijders “Estimation and prediction for stochastic blockstructures” In Journal of the American statistical association 96.455 Taylor & Francis, 2001, pp. 1077–1087
  28. Lizhi Zhang and Tiago P. Peixoto “Statistical Inference of Assortative Community Structures” In Physical Review Research 2.4, 2020, pp. 043271 DOI: 10.1103/PhysRevResearch.2.043271
  29. Ryan J. Gallagher, Jean-Gabriel Young and Brooke Foucault Welles “A Clarified Typology of Core-Periphery Structure in Networks” In Science Advances 7.12 American Association for the Advancement of Science, 2021, pp. eabc9800 DOI: 10.1126/sciadv.abc9800
  30. Tiago P. Peixoto and Alec Kirkley “Implicit Models, Latent Compression, Intrinsic Biases, and Cheap Lunches in Community Detection” arXiv, 2023 arXiv:arXiv:2210.09186
  31. “Universality of the Stochastic Block Model” In Physical Review E 98.3, 2018, pp. 032309 DOI: 10.1103/PhysRevE.98.032309
  32. Tiago P. Peixoto “Entropy of Stochastic Blockmodel Ensembles” In Physical Review E 85.5, 2012, pp. 056122 DOI: 10.1103/PhysRevE.85.056122
  33. Tiago P. Peixoto “Descriptive vs. Inferential Community Detection: Pitfalls, Myths and Half-Truths” In arXiv:2112.00183 [physics, stat], 2022 arXiv:2112.00183 [physics, stat]
  34. Tiago P. Peixoto “Hierarchical Block Structures and High-Resolution Model Selection in Large Networks” In Physical Review X 4.1 APS, 2014, pp. 011047 DOI: 10.1103/PhysRevX.4.011047
  35. Emmanuel Lazega “The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership” Oxford University Press, USA, 2001
  36. “Social finance and impact investing. Governing welfare in the era of financialization” In Historical Social Research/Historische Sozialforschung 45.3 JSTOR, 2020, pp. 7–30
  37. “Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship” In Proceedings of the 2018 world wide web conference, 2018, pp. 913–922
  38. Marilena Hohmann, Karel Devriendt and Michele Coscia “Quantifying Ideological Polarization on a Network Using Generalized Euclidean Distance” In Science Advances 9.9 American Association for the Advancement of Science, 2023, pp. eabq2044 DOI: 10.1126/sciadv.abq2044
  39. “The Echo Chamber Effect on Social Media” In Proceedings of the National Academy of Sciences 118.9 Proceedings of the National Academy of Sciences, 2021, pp. e2023301118 DOI: 10.1073/pnas.2023301118
  40. Tiago P. Peixoto “Parsimonious Module Inference in Large Networks” In Physical review letters 110.14 APS, 2013, pp. 148701
  41. Ginestra Bianconi “Entropy of Network Ensembles” In Physical Review E 79.3, 2009, pp. 036114 DOI: 10.1103/PhysRevE.79.036114
  42. Anne Condon and Richard M. Karp “Algorithms for Graph Partitioning on the Planted Partition Model” In Random Structures & Algorithms 18.2, 2001, pp. 116–140 DOI: 10.1002/1098-2418(200103)18:2<116::AID-RSA1001>3.0.CO;2-2
  43. Brian Karrer, Elizaveta Levina and Mark EJ Newman “Robustness of Community Structure in Networks” In Physical review E 77.4 APS, 2008, pp. 046119
  44. Andrea Lancichinetti, Santo Fortunato and Filippo Radicchi “Benchmark graphs for testing community detection algorithms” In Physical review E 78.4 APS, 2008, pp. 046110
  45. “Community Detection Algorithms: A Comparative Analysis” In Physical Review E 80.5, 2009, pp. 056117 DOI: 10.1103/PhysRevE.80.056117
  46. Aric Hagberg, Pieter Swart and Daniel S Chult “Exploring network structure, dynamics, and function using NetworkX”, 2008

Summary

We haven't generated a summary for this paper yet.