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The ground truth about metadata and community detection in networks

Bibliographic reference Peel, Leto ; Larremore, Daniel B. ; Clauset, Aaron. The ground truth about metadata and community detection in networks. In: Science Advances, Vol. 3, no.5, p. e1602548 (2017)
Permanent URL http://hdl.handle.net/2078.1/184574
  1. Evans T S, Clique graphs and overlapping communities, 10.1088/1742-5468/2010/12/p12037
  2. Adamic Lada A., Glance Natalie, The political blogosphere and the 2004 U.S. election : divided they blog, 10.1145/1134271.1134277
  3. Hric Darko, Darst Richard K., Fortunato Santo, Community detection in networks: Structural communities versus ground truth, 10.1103/physreve.90.062805
  4. Leskovec Jure, Lang Kevin J., Mahoney Michael, Empirical comparison of algorithms for network community detection, 10.1145/1772690.1772755
  5. Yang Jaewon, Leskovec Jure, Community-Affiliation Graph Model for Overlapping Network Community Detection, 10.1109/icdm.2012.139
  6. Zachary Wayne W., An Information Flow Model for Conflict and Fission in Small Groups, 10.1086/jar.33.4.3629752
  7. Holland Paul W., Laskey Kathryn Blackmond, Leinhardt Samuel, Stochastic blockmodels: First steps, 10.1016/0378-8733(83)90021-7
  8. Nowicki Krzysztof, Snijders Tom A. B, Estimation and Prediction for Stochastic Blockstructures, 10.1198/016214501753208735
  9. Jianbo Shi, Malik J., Normalized cuts and image segmentation, 10.1109/34.868688
  10. Cheng Xue-Qi, Shen Hua-Wei, Uncovering the community structure associated with the diffusion dynamics on networks, 10.1088/1742-5468/2010/04/p04024
  11. Krzakala F., Moore C., Mossel E., Neeman J., Sly A., Zdeborova L., Zhang P., Spectral redemption in clustering sparse networks, 10.1073/pnas.1312486110
  12. Fortunato Santo, Community detection in graphs, 10.1016/j.physrep.2009.11.002
  13. Karrer Brian, Newman M. E. J., Stochastic blockmodels and community structure in networks, 10.1103/physreve.83.016107
  14. Newman M. E. J., Clauset Aaron, Structure and inference in annotated networks, 10.1038/ncomms11863
  15. Good Benjamin H., de Montjoye Yves-Alexandre, Clauset Aaron, Performance of modularity maximization in practical contexts, 10.1103/physreve.81.046106
  16. Bordenave Charles, Lelarge Marc, Massoulie Laurent, Non-backtracking Spectrum of Random Graphs: Community Detection and Non-regular Ramanujan Graphs, 10.1109/focs.2015.86
  17. Ghasemian, Phys. Rev. X, 6, 031005 (2016)
  18. Massoulié Laurent, Community detection thresholds and the weak Ramanujan property, 10.1145/2591796.2591857
  19. E. Mossel, J. Neeman, A. Sly, Belief propagation, robust reconstruction and optimal recovery of block models, Proceedings of the 27th Conference on Learning Theory, Barcelona, Spain, 13 to 15 June 2014, vol. 35, pp. 356–370.
  20. Mossel Elchanan, Neeman Joe, Sly Allan, Reconstruction and estimation in the planted partition model, 10.1007/s00440-014-0576-6
  21. Guimerà Roger, Sales-Pardo Marta, Amaral Luís A. Nunes, Modularity from fluctuations in random graphs and complex networks, 10.1103/physreve.70.025101
  22. D. Taylor , R. S. Caceres , P. J. Mucha , Detectability of small communities in multilayer and temporal networks: Eigenvector localization, layer aggregation, and time series discretization. arXiv:1609.04376 (2016).
  23. Lancichinetti Andrea, Fortunato Santo, Community detection algorithms: A comparative analysis, 10.1103/physreve.80.056117
  24. Holme P., Huss M., Jeong H., Subnetwork hierarchies of biochemical pathways, 10.1093/bioinformatics/btg033
  25. Yang Jaewon, Leskovec Jure, Defining and evaluating network communities based on ground-truth, 10.1007/s10115-013-0693-z
  26. Wolpert David H., The Lack of A Priori Distinctions Between Learning Algorithms, 10.1162/neco.1996.8.7.1341
  27. L. Peel, Topological feature based classification, Proceedings of the 14th International Conference on Information Fusion, Chicago, IL, 5 to 8 July 2011 (IEEE, 2011), pp. 1–8.
  28. L. Peel , Supervised blockmodelling. arXiv:1209.5561 (2012).
  29. Fosdick Bailey K., Hoff Peter D., Testing and Modeling Dependencies Between a Network and Nodal Attributes, 10.1080/01621459.2015.1008697
  30. Airoldi, J. Mach. Learn. Res., 9, 1981 (2007)
  31. Ball Brian, Karrer Brian, Newman M. E. J., Efficient and principled method for detecting communities in networks, 10.1103/physreve.84.036103
  32. Larremore Daniel B., Clauset Aaron, Jacobs Abigail Z., Efficiently inferring community structure in bipartite networks, 10.1103/physreve.90.012805
  33. Peixoto, Phys. Rev. X, 4, 011047 (2014)
  34. Guimerà Roger, Nunes Amaral Luís A., Functional cartography of complex metabolic networks, 10.1038/nature03288
  35. Bianconi Ginestra, Pin Paolo, Marsili Matteo, Assessing the relevance of node features for network structure, 10.1073/pnas.0811511106
  36. Larremore Daniel B., Clauset Aaron, Buckee Caroline O., A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes, 10.1371/journal.pcbi.1003268
  37. Ahn Yong-Yeol, Bagrow James P., Lehmann Sune, Link communities reveal multiscale complexity in networks, 10.1038/nature09182
  38. Soundarajan Sucheta, Hopcroft John, Using community information to improve the precision of link prediction methods, 10.1145/2187980.2188150
  39. Chakrabort Tanmoy, Sikdar Sandipan, Tammana Vihar, Ganguly Niloy, Mukherjee Animesh, Computer science fields as ground-truth communities : their impact, rise and fall, 10.1145/2492517.2492536
  40. von Luxburg, J. Mach. Learn. Res., 27, 65 (2012)
  41. J. Kleinberg , An impossibility theorem for clustering. Adv. Neural Inf. Process. Syst. 463–470 (2003).
  42. A. Browet , J. M. Hendrickx , A. Sarlette , Incompatibility boundaries for properties of community partitions. arXiv:1603.00621 (2016).
  43. Clauset Aaron, Moore Cristopher, Newman M. E. J., Hierarchical structure and the prediction of missing links in networks, 10.1038/nature06830
  44. Peel, J. Adv. Inf. Fusion, 6, 119 (2011)
  45. Yang Zhao, Algesheimer René, Tessone Claudio J., A Comparative Analysis of Community Detection Algorithms on Artificial Networks, 10.1038/srep30750
  46. C. Cortes, D. Pregibon, C. Volinsky, Communities of interest, in Advances in Intelligent Data Analysis, vol. 2189 of Lecture Notes in Computer Science, F. Hoffmann, D. Hand, N. Adams, D. Fisher, G. Guimaraes, Eds. (Springer, 2001), pp. 105–114.
  47. D. Hric , T. P. Peixoto , S. Fortunato , Network structure, metadata and the prediction of missing nodes. arXiv:1604.00255 (2016).
  48. Lazega Emmanuel, The Collegial Phenomenon, ISBN:9780199242726, 10.1093/acprof:oso/9780199242726.001.0001
  49. Peixoto Tiago P., Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models, 10.1103/physreve.89.012804
  50. Peixoto Tiago P., Entropy of stochastic blockmodel ensembles, 10.1103/physreve.85.056122
  51. J. Parkkinen, J. Sinkkonen, A. Gyenge, S. Kaski, A block model suitable for sparse graphs, Proceedings of the 7th International Workshop on Mining and Learning with Graphs (MLG 2009), Leuven, Belgium, 2 to 4 July 2009, vol. 5.
  52. J. Cichoń, Z. Gołe¸biewski, On Bernoulli sums and Bernstein polynomials, 23rd International Meeting on Probabilistic, Combinatorial, and Asymptotic Methods in the Analysis of Algorithms (AofA’12), Montreal, Quebec, Canada, 18 to 22 June 2012, pp. 179–190.
  53. U. Brandes , D. Delling , M. Gaertler , R. Goerke , M. Hoefer , Z. Nikoloski , D. Wagner , Maximizing modularity is hard. arXiv:physics/0608255 (2006).
  54. Vinh Nguyen Xuan, Epps Julien, Bailey James, Information theoretic measures for clusterings comparison : is a correction for chance necessary?, 10.1145/1553374.1553511
  55. I. Borg, P. J. F. Groenen, Modern Multidimensional Scaling: Theory and Applications (Springer Science & Business Media, 2005).
  56. Haggerty Leanne S., Jachiet Pierre-Alain, Hanage William P., Fitzpatrick David A., Lopez Philippe, O’Connell Mary J., Pisani Davide, Wilkinson Mark, Bapteste Eric, McInerney James O., A Pluralistic Account of Homology: Adapting the Models to the Data, 10.1093/molbev/mst228
  57. Meilă Marina, Comparing Clusterings by the Variation of Information, Learning Theory and Kernel Machines (2003) ISBN:9783540407201 p.173-187, 10.1007/978-3-540-45167-9_14
  58. Erdős, Publ. Math. Debrecen, 6, 290 (1959)
  59. Decelle Aurelien, Krzakala Florent, Moore Cristopher, Zdeborová Lenka, Inference and Phase Transitions in the Detection of Modules in Sparse Networks, 10.1103/physrevlett.107.065701
  60. Girvan M., Newman M. E. J., Community structure in social and biological networks, 10.1073/pnas.122653799