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Reading Lists on Community Detection

Social Network Books

  1. S. Wasserman, “Social network analysis: Methods and applications,” vol. 1, p. 116, 1994.
  2. R. a Hanneman, “Introduction to Social Network Methods,” Network, vol. 2, no. 3, pp. 292–1990, 1998.
  3. D. Easley and J. Kleinberg, “Networks , Crowds , and Markets : Reasoning about a Highly Connected World,” Science (801.. )., vol. 81, p. 744, 2010.
  4. A. Rajaraman and J. D. Ullman, “Mining of Massive Datasets,” Lect. Notes Stanford CS345A Web Min., vol. 67, p. 328, 2011.
  5. M. a Russell, “Mining the Social Web,” Book, vol. 54, no. 2, pp. 5623–5633, 2011.
  6. R. Bott, Social Network Data Analytics, no. 1. 2014.
  7. A. Abraham and A.1.E. Hassanien, Computational social Network Analysis: Trends, Tools and Research Advances. .
  8. “Social network analysis methods and applications.pdf.” .
  9. S. Havlin and R. Cohen, Complex Networks, structue, robustness and function. .

Surveys

  1. S. Harenberg, G. Bello, L. Gjeltema, S. Ranshous, J. Harlalka, R. Seay, K. Padmanabhan, and N. Samatova, “Community detection in large1.scale networks: a survey and empirical evaluation,” Wiley Interdiscip. Rev. Comput. Stat., vol. 6, no. 6, pp. 426–439, 2014.
  2. J. Xie, S. Kelley, and B. K. Szymanski, “Overlapping community detection in networks,” ACM Comput. Surv., vol. 45, no. 4, pp. 1–35, 2013.
  3. M. Planti and M. Crampes, “Survey on Social Community Detection To cite this version : Survey on Social Community Detection,” 2013.
  4. Y. Zhu, B. Xu, X. Shi, and Y. Wang, “A survey of social1.based routing in delay tolerant networks: Positive and negative social effects,” IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 387–401, 2013.
  5. S. Papadopoulos, Y. Kompatsiaris, A. Vakali, and P. Spyridonos, “Community detection in Social Media,” Data Min. Knowl. Discov., vol. 24, no. 3, pp. 515–554, 2012.
  6. P. J. Bickel, A. Chen, and E. Levina, “The method of moments and degree distributions for network models,” Ann. Stat., vol. 39, no. 5, pp. 2280–2301, 2011.
  7. L. Lü and T. Zhou, “Link prediction in complex networks: A survey,” Phys. A Stat. Mech. its Appl., vol. 390, no. 6, pp. 1150–1170, 2011.
  8. J. Leskovec, K. J. Lang, and M. Mahoney, “Empirical comparison of algorithms for network community detection,” Proc. 19th Int. World Wide Web, pp. 631–640, 2010.
  9. S. Fortunato, “Community detection in graphs,” Phys. Rep., vol. 486, no. 3–5, pp. 75–174, 2010.
  10. A. Lancichinetti and S. Fortunato, “Community detection algorithms: a comparative analysis,” Phys Rev E Stat Nonlin Soft Matter Phys, vol. 80, no. 5 Pt 2, p. 56117, 2009.

Benchmarks

  1. M. Wang, C. Wang, J. X. Yu, and J. Zhang, “Community Detection in Social Networks : An In1.depth Benchmarking Study with a Procedure1.Oriented Framework,” Proc. VLDB Endow., pp. 998–1009, 2015.
  2. P. Zhang, “Evaluating accuracy of community detection using the relative normalized mutual information,” Physics.soc1.ph, pp. 1–7, 2015.
  3. C. Lee and P. Cunningham, “Community detection: effective evaluation on large social networks,” J. Complex Networks, vol. 2, no. 1, pp. 19–37, 2013.
  4. R. Aldecoa and I. Marín, “Exploring the limits of community detection strategies in complex networks,” Sci. Rep., vol. 3, pp. 8–11, 2013.
  5. R. Aldecoa, I. Marín, I. Marin, and I. Mar, “Closed benchmarks for network community structure characterization,” Phys. Rev. E, vol. 85, no. 2, p. 26109, 2012.
  6. A. Prat1.Pérez, D. Dominguez1.Sal, J. M. Brunat, and J.1.L. Larriba1.Pey, “Shaping communities out of triangles,” Proc. 21st ACM Int. Conf. Inf. Knowl. Manag. 1. CIKM ’12, p. 1677, 2012.
  7. a. F. McDaid, D. Greene, and N. Hurley, “Normalized Mutual Information to evaluate overlapping community finding algorithms,” Arxiv Prepr. arXiv1110.2515, pp. 1–3, 2011.
  8. J. Leskovec, K. J. Lang, and M. Mahoney, “Empirical comparison of algorithms for network community detection,” Proc. 19th Int. World Wide Web, pp. 631–640, 2010.
  9. A. Lancichinetti, S. Fortunato, and J. Kertész, “Detecting the overlapping and hierarchical community structure in complex networks,” New J. Phys., vol. 11, pp. 1–18, 2009.
  10. A. Lancichinetti, S. Fortunato, and F. Radicchi, “Benchmark graphs for testing community detection algorithms,” Phys. Rev. E, vol. 78, no. 4, p. 046110, 2008.
  11. M. Meilă, “Comparing clusterings—an information based distance,” J. Multivar. Anal., vol. 98, no. 5, pp. 873–895, 2007.
  12. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, “Uncovering the overlapping community structure of complex networks in nature and society,” Nature, vol. 435, no. 7043, pp. 814–8, 2005.
  13. R. Guimerà, M. Sales1.Pardo, and L. A. N. Amaral, “Modularity from fluctuations in random graphs and complex networks,” Phys. Rev. E, vol. 70, no. 2, p. 025101, 2004.

Statistical(Model-based) Algorithms

  1. C. Gao, Z. Ma, A. Y. Zhang, and H. H. Zhou, “Achieving Optimal Misclassification Proportion in Stochastic Block Model,” pp. 1–41, 2015.
  2. J. Jin, “Fast community detection by SCORE,” Ann. Stat., vol. 43, no. 1, pp. 57–89, 2015.
  3. J. Lei and A. Rinaldo, “Consistency of spectral clustering in stochastic block models,” Ann. Stat., vol. 43, no. 1, pp. 215–237, 2015.
  4. S. Chatterjee, “Matrix estimation by Universal Singular Value Thresholding,” Ann. Stat., vol. 43, no. 1, pp. 177–214, 2015.
  5. A. A. Amini and E. Levina, “On semidefinite relaxations for the block model,” pp. 1–29, 2014.
  6. D. E. Fishkind, D. L. Sussman, M. Tang, J. T. Vogelstein, and C. E. Priebe, “Downloaded 01 / 20 / 16 to 202 . 40 . 139 . 167 . Redistribution subject to SIAM license or copyright ; see http://www.siam.org/journals/ojsa.php STOCHASTIC BLOCK MODEL WHEN THE MODEL PARAMETERS Copyright © by SIAM . Unauthorized reproduction of this arti,” vol. 34, no. 1, pp. 23–39, 2013.
  7. E. Mossel, J. Neeman, and A. Sly, A Proof Of The Block Model Threshold Conjecture. 2013.
  8. T. Qin and K. Rohe, “Regularized spectral clustering under the degree1.corrected stochastic blockmodel,” Adv. Neural Inf. Process. Syst., pp. 1–9, 2013.
  9. A. a. Amini, A. Chen, P. J. Bickel, and E. Levina, “Pseudo1.likelihood methods for community detection in large sparse networks,” Ann. Stat., vol. 41, no. 4, pp. 2097–2122, 2013.
  10. D. S. Choi, P. J. Wolfe, and E. M. Airoldi, “Stochastic blockmodels with a growing number of classes,” Biometrika, vol. 99, no. 2, pp. 273–284, 2012.
  11. J. Chen and Y. Saad, “Dense subgraph extraction with application to community detection,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 7, pp. 1216–1230, 2012.
  12. A. Celisse, J.1.J. Daudin, and L. Pierre, “Consistency of maximum1.likelihood and variational estimators in the stochastic block model,” Electron. J. Stat., vol. 6, no. 0, pp. 1847–1899, 2012.
  13. K. Chaudhuri, F. Chung, and A. Tsiatas, “Spectral clustering of graphs with general degrees in the extended planted partition model,” J. Mach. Learn. Res., vol. 35, no. d, pp. 1–23, 2012.
  14. E. Mossel, J. Neeman, and A. Sly, Stochastic Block Models and Reconstruction, vol. 2. 2012.
  15. A. Jalali, Y. Chen, S. Sanghavi, and H. Xu, “Clustering Partially Observed Graphs via Convex Optimization,” vol. 15, pp. 2213–2238, 2011.
  16. K. Rohe, S. Chatterjee, and B. Yu, “Spectral clustering and the high1.dimensional stochastic blockmodel,” Ann. Stat., vol. 39, no. 4, pp. 1878–1915, 2011.
  17. D. Cai, X. He, J. Han, and T. S. Huang, “Graph Regularized Non1.Negative Matrix Factorization for Data Representation.,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1548–1560, 2010.
  18. U. von Luxburg, “A tutorial on spectral clustering,” Stat. Comput., no. August 2006, pp. 1–32, 2007.
  19. L. Donetti and M. A. Munoz, “Improved spectral algorithm for the detection of network communities,” Aip Conf. Proc., p. 4, 2005.

Non-Statistical Algorithms

  1. S.1.H. Bae and B. Howe, “GossipMap: a distributed community detection algorithm for billion1.edge directed graphs,” SC 2015, p. 27, 2015.
  2. M. Mitzenmacher, J. Pachocki, R. Peng, C. Tsourakakis, and S. C. Xu, “Scalable Large Near1.Clique Detection in Large1.Scale Networks via Sampling,” Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 1. KDD ’15, pp. 815–824, 2015.
  3. a Prat1.Pérez, “High Quality , Scalable and Parallel Community Detection for Large Real Graphs,” Proc. 23rd …, pp. 225–235, 2014.
  4. T. Cai and X. Li, “Robust and computationally feasible community detection in the presence of arbitrary outlier nodes,” arXiv Prepr., vol. 43, no. 3, pp. 1–38, 2014.
  5. S. Sobolevsky, R. Campari, A. Belyi, and C. Ratti, “General optimization technique for high1.quality community detection in complex networks,” Phys. Rev. E, vol. 90, no. 1, p. 012811, 2014.
  6. I. M. Kloumann, R. Hall, J. M. Kleinberg, and G. Hall, “Community Membership Identification from Small Seed Sets,” Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. 1. KDD ’14, pp. 1366–1375, 2014.
  7. S. Lim, S. Ryu, S. Kwon, K. Jung, and J. G. Lee, “LinkSCAN*: Overlapping community detection using the link1.space transformation,” Proc. 1. Int. Conf. Data Eng., pp. 292–303, 2014.
  8. C. Peng, T. G. Kolda, and A. Pinar, “Accelerating Community Detection by Using K1.core Subgraphs,” p. 10, 2014.
  9. R. Aldecoa and I. Marín, “Surprise maximization reveals the community structure of complex networks.,” Sci. Rep., vol. 3, p. 1060, 2013.
  10. A. K. H. Tung, “Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis,” Proc. VLDB Endow., vol. 6, no. 2, pp. 85–96, 2013.
  11. E. Le Martelot and C. Hankin, “Fast Multi1.Scale Detection of Relevant Communities in Large1.Scale Networks,” Comput. J., vol. 56, no. 9, pp. 1136–1150, 2013.
  12. Y. Ruan, D. Fuhry, and S. Parthasarathy, “Efficient Community Detection in Large Networks using Content and Links,” Int. World Wide Web Conf. Steer. Comm., pp. 1089–1098, 2013.
  13. J. J. Whang, D. F. Gleich, and I. S. Dhillon, “Overlapping community detection using seed set expansion,” Proc. 22nd ACM Int. Conf. Conf. Inf. Knowl. Manag. 1. CIKM ’13, pp. 2099–2108, 2013.
  14. Kunpeng Zhang, D. Downey, Zhengzhang Chen, Yusheng Xie, Yu Cheng, A. Agrawal, Wei1.keng Liao, A. Choudhary, K. Zhang, Z. Chen, Y. Xie, Y. Cheng, W. Liao, M. Ovelg, C. Thovex, F. Trichet, G. Praprotnik, T. Ivanusa, I. Podbregar, P. P. Paul, M. Gavrilova, T. H. Nguyen, B. K. Szymanski, O. Janssens, M. Slembrouck, S. Verstockt, S. Van Hoecke, R. Van de Walle, U. R. Hodeghatta, J. D. Cruz, C. Bothorel, F. Poulet, M. Baratchi, N. Meratnia, P. J. M. Havinga, I. Weber, and K. Garimella, “A probabilistic graphical model for brand reputation assessment in social networks,” Proc. 2013 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min. 1. ASONAM ’13, pp. 223–230, 2013.
  15. S. H. Bae, D. Halperin, J. West, M. Rosvall, and B. Howe, “Scalable flow1.based community detection for large1.scale network analysis,” Proc. 1. IEEE 13th Int. Conf. Data Min. Work. ICDMW 2013, pp. 303–310, 2013.
  16. C. L. Staudt and H. Meyerhenke, “Engineering Parallel Algorithms for Community Detection in Massive Networks,” pp. 1–14, 2013.
  17. J. Huang, H. Sun, Q. Song, H. Deng, and J. Han, “Revealing density1.based clustering structure from the core1.connected tree of a network,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 8, pp. 1876–1889, 2013.
  18. C. L. Staudt and H. Meyerhenke, “Engineering high1.Performance community detection heuristics for massive graphs,” Proc. Int. Conf. Parallel Process., pp. 180–189, 2013.
  19. W. Cui, Y. Xiao, H. Wang, Y. Lu, and W. Wang, “Online search of overlapping communities,” Proc. 2013 Int. Conf. Manag. data 1. SIGMOD ’13, p. 277, 2013.
  20. H. Shiokawa, Y. Fujiwara, and M. Onizuka, “Fast Algorithm for Modularity1.Based Graph Clustering.,” Aaai, 2013.
  21. C. Tsourakakis, F. Bonchi, A. Gionis, F. Gullo, and M. Tsiarli, “Denser than the densest subgraph,” Proc. 19th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. 1. KDD ’13, p. 104, 2013.
  22. L. Waltman and N. J. Van Eck, “A smart local moving algorithm for large1.scale modularity1.based community detection,” Eur. Phys. J. B, vol. 86, no. 11, pp. 1–33, 2013.
  23. J. Yang and J. Leskovec, “Overlapping community detection at scale: A Nonnegative Matrix Factorization Approach,” Sixth ACM Int. Conf. Web search data Min., p. 587, 2013.
  24. F. Reid, A. Mcdaid, and N. Hurley, “Percolation computation in complex networks,” Proc. 2012 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2012, pp. 274–281, 2012.
  25. J. Yang and J. Leskovec, “Structure and Overlaps of Ground1.Truth Communities in Networks,” 6th SNA1.KDD Work. ’12 (SNA1.KDD’12), August 12, 2012, Beijing, China, vol. 5, no. 2, pp. 1–35, 2012.
  26. J. Riedy, D. A. Bader, and H. Meyerhenke, “Scalable Multi1.threaded Community Detection in Social Networks,” 2012 IEEE 26th Int. Parallel Distrib. Process. Symp. Work. PhD Forum, pp. 1619–1628, 2012.
  27. W. Lin, X. Kong, P. S. Yu, Q. Wu, Y. Jia, and C. Li, “Community detection in incomplete information networks,” Www2012, p. 341, 2012.
  28. Y. Chen, S. Sanghavi, and H. Xu, “Clustering Sparse Graphs,” Adv. Neural Inf. Process. Syst. 25 (Proceedings NIPS), pp. 1–9, 2012.
  29. J. Yang and J. Leskovec, “Community1.affiliation graph model for overlapping network community detection,” Proc. 1. IEEE Int. Conf. Data Mining, ICDM, vol. 1, pp. 1170–1175, 2012.
  30. A. Lancichinetti and S. Fortunato, “Consensus clustering in complex networks.,” Sci. Rep., vol. 2, p. 336, 2012.
  31. P. Sarkar, M. I. Jordan, and D. Chakrabarti, “Nonparametric Link Prediction in Dynamic Networks,” Proc. 29th Int. Conf. Mach. Learn., pp. 1687–1694, 2012.
  32. I. Psorakis, “Overlapping community detection using Bayesian non1.negative matrix factorization,” Phys. Rev. E, vol. 83, 2011.
  33. M. Rosvall and C. T. Bergstrom, “Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems.,” PLoS One, vol. 6, no. 4, p. e18209, 2011.
  34. P. Ronhovde and Z. Nussinov, “Local resolution1.limit1.free Potts model for community detection,” Phys. Rev. E 1. Stat. Nonlinear, Soft Matter Phys., vol. 81, no. 4, pp. 1–15, 2010.
  35. J. Leskovec, D. Huttenlocher, and J. Kleinberg, “Predicting Positive and Negative Links,” Int. World Wide Web Conf., pp. 641–650, 2010.
  36. Y.1.Y. Ahn, J. P. Bagrow, and S. Lehmann, “Link communities reveal multiscale complexity in networks,” Nature, vol. 466, no. 7307, pp. 761–764, 2010.
  37. J. Liu and T. Liu, “Detecting community structure in complex networks using simulated annealing with 1.means algorithms,” Phys. A Stat. Mech. its Appl., vol. 389, no. 11, pp. 2300–2309, 2010.
  38. R. R. Khorasgani, J. Chen, and O. R. Zaïane, “Top Leaders Community Detection Approach in Information Networks,” Proc. 4th Work. Soc. Netw. Min. Anal., no. July, pp. 1 – 9, 2010.
  39. S. Gregory, “Finding overlapping communities in networks by label propagation,” New J. Phys., vol. 12, no. 10, p. 103018, 2010.
  40. A. F. McDaid and N. J. Hurley, “Using Model1.based Overlapping Seed Expansion to detect highly overlapping community structure,” Int. Conf. Adv. Soc. Networks Anal. Min., pp. 1–23, 2010.
  41. D. Bortner and J. Han, “Progressive clustering of networks using structure1.connected order of traversal,” Proc. 1. Int. Conf. Data Eng., pp. 653–656, 2010.
  42. C. Lee, F. Reid, A. McDaid, and N. Hurley, “Detecting highly overlapping community structure by greedy clique expansion,” vol. 10, 2010.
  43. I. X. Y. Leung, P. Hui, P. Liò, and J. Crowcroft, “Towards real1.time community detection in large networks,” Phys. Rev. E, vol. 79, no. 6, p. 066107, 2009.
  44. Y. Zhou, H. Cheng, and J. X. Yu, “Graph Clustering Based on Structural / Attribute Similarities,” Vldb, vol. 2, no. 1, pp. 718–729, 2009.
  45. P. Ronhovde and Z. Nussinov, “Multiresolution community detection for megascale networks by information1.based replica correlations,” Phys. Rev. E, vol. 80, no. 1, pp. 1–18, 2009.
  46. V. D. Blondel, J.1.L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech. Theory Exp., vol. 10008, no. 10, p. 6, 2008.
  47. V. D. Blondel, J.1.L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech. Theory Exp., vol. 10008, no. 10, p. 6, 2008.
  48. M. Rosvall and C. T. Bergstrom, “Maps of random walks on complex networks reveal community structure.,” Proc. Natl. Acad. Sci. U. S. A., vol. 105, no. 4, pp. 1118–23, 2008.
  49. J. M. Kumpula, M. Kivelä, K. Kaski, and J. Saramäki, “Sequential algorithm for fast clique percolation.,” Phys. Rev. E. Stat. Nonlin. Soft Matter Phys., vol. 78, no. 2 Pt 2, p. 026109, 2008.
  50. R. Guimera, A. Moreira, D. Rodrigues, E. Mar, R. Bateman, E. D. Rodrigues, P. Pallai, K. M. Shokat, J. D. Baxter, R. Kiplin, P. Webb, R. J. Fletterick, and M. Sales1.pardo, “Extracting the hierarchical organization,” Proc. Natl. Acad. Sci., vol. 104, no. 39, pp. 15224–15229, 2007.
  51. U. N. Raghavan, R. Albert, and S. Kumara, “Near linear time algorithm to detect community structures in large1.scale networks,” Phys. Rev. E 1. Stat. Nonlinear, Soft Matter Phys., vol. 76, no. 3, pp. 1–11, 2007.
  52. P. Pons and M. Latapy, “Computing communities in large networks using random walks,” J. Graph Algorithms Appl., vol. 10, no. 2, pp. 191–218, 2005.
  53. J. Duch and A. Arenas, “Community detection in complex networks using extremal optimization,” Phys. Rev. E, vol. 72, no. 2, p. 027104, 2005.
  54. P. Pons and M. Latapy, “Computing Communities in Large Networks Using Random Walks,” Comput. Inf. Sci. 1. Isc. 2005, pp. 284–293, 2005.
  55. A. Clauset, M. E. J. Newman, and C. Moore, “Finding community structure in very large networks,” Phys. Rev. E, vol. 70, no. 6, p. 066111, 2004.