Community detection and stochastic block models: recent developments
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[1] B. Bollobás,et al. The phase transition in inhomogeneous random graphs , 2007 .
[2] Can M. Le,et al. Sparse random graphs: regularization and concentration of the Laplacian , 2015, ArXiv.
[3] Patrick J. Wolfe,et al. Network histograms and universality of blockmodel approximation , 2013, Proceedings of the National Academy of Sciences.
[4] Emmanuel Abbe,et al. Community Detection in General Stochastic Block models: Fundamental Limits and Efficient Algorithms for Recovery , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.
[5] Christian Borgs,et al. Private Graphon Estimation for Sparse Graphs , 2015, NIPS.
[6] Y. Peres,et al. Broadcasting on trees and the Ising model , 2000 .
[7] Yi-Cheng Zhang,et al. Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[8] William W. Cohen,et al. Community-Based Recommendations: a Solution to the Cold Start Problem , 2011 .
[9] Emmanuel Abbe,et al. Graph compression: The effect of clusters , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[10] Greg Linden,et al. Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .
[11] Frank Thomson Leighton,et al. Graph Bisection Algorithms with Good Average Case Behavior , 1984, FOCS.
[12] X ZhengAlice,et al. A Survey of Statistical Network Models , 2010 .
[13] Ravi B. Boppana,et al. Eigenvalues and graph bisection: An average-case analysis , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[14] Marc Lelarge,et al. Recovering Asymmetric Communities in the Stochastic Block Model , 2018, IEEE Transactions on Network Science and Engineering.
[15] Noga Alon,et al. Finding a large hidden clique in a random graph , 1998, SODA '98.
[16] Kathryn B. Laskey,et al. Stochastic blockmodels: First steps , 1983 .
[17] Jingchun Chen,et al. Detecting functional modules in the yeast protein-protein interaction network , 2006, Bioinform..
[18] D. Eisenberg,et al. Detecting protein function and protein-protein interactions from genome sequences. , 1999, Science.
[19] Van H. Vu,et al. Spectral norm of random matrices , 2005, STOC '05.
[20] Emmanuel Abbe,et al. Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters , 2015, NIPS.
[21] C. Borgs,et al. Consistent nonparametric estimation for heavy-tailed sparse graphs , 2015, The Annals of Statistics.
[22] Jiashun Jin,et al. FAST COMMUNITY DETECTION BY SCORE , 2012, 1211.5803.
[23] Thomas Bonald,et al. A spectral algorithm with additive clustering for the recovery of overlapping communities in networks , 2018, Theor. Comput. Sci..
[24] P. Rigollet,et al. Optimal detection of sparse principal components in high dimension , 2012, 1202.5070.
[25] Bruce E. Hajek,et al. Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions , 2015, IEEE Transactions on Information Theory.
[26] M. Bálek,et al. Large Networks and Graph Limits , 2022 .
[27] A. Rbnyi. ON THE EVOLUTION OF RANDOM GRAPHS , 2001 .
[28] Assaf Naor,et al. Rigorous location of phase transitions in hard optimization problems , 2005, Nature.
[29] van Vu,et al. A Simple SVD Algorithm for Finding Hidden Partitions , 2014, Combinatorics, Probability and Computing.
[30] Béla Bollobás,et al. The phase transition in inhomogeneous random graphs , 2007, Random Struct. Algorithms.
[31] V. Sós,et al. Convergent Sequences of Dense Graphs I: Subgraph Frequencies, Metric Properties and Testing , 2007, math/0702004.
[32] Alexandra Kolla,et al. Multisection in the Stochastic Block Model using Semidefinite Programming , 2015, ArXiv.
[33] Cristopher Moore,et al. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[34] Andrea Montanari,et al. Conditional Random Fields, Planted Constraint Satisfaction and Entropy Concentration , 2013, APPROX-RANDOM.
[35] M. Newman,et al. Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[36] Piyush Srivastava,et al. Exact recovery in the Ising blockmodel , 2016, The Annals of Statistics.
[37] Marc Lelarge,et al. Fundamental limits of symmetric low-rank matrix estimation , 2016, Probability Theory and Related Fields.
[38] Edoardo M. Airoldi,et al. Stochastic blockmodels with growing number of classes , 2010, Biometrika.
[39] Robert Dondero. Princeton University , 2001 .
[40] Cristopher Moore,et al. Phase transitions in semisupervised clustering of sparse networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[41] Florent Krzakala,et al. MMSE of probabilistic low-rank matrix estimation: Universality with respect to the output channel , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[42] Sergio Verdú,et al. Compressing data on graphs with clusters , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).
[43] D. Welsh,et al. A Spectral Technique for Coloring Random 3-Colorable Graphs , 1994 .
[44] Emmanuel Abbe,et al. Proof of the Achievability Conjectures for the General Stochastic Block Model , 2018 .
[45] Assaf Naor,et al. The two possible values of the chromatic number of a random graph , 2004, STOC '04.
[46] B. Bollobás. The evolution of random graphs , 1984 .
[47] Emmanuel Abbe,et al. Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation , 2016, NIPS.
[48] Peter J. Bickel,et al. Community Detection in Networks using Graph Distance , 2014, ArXiv.
[49] Andrea Montanari,et al. Finding One Community in a Sparse Graph , 2015, Journal of Statistical Physics.
[50] E. Szemerédi. Regular Partitions of Graphs , 1975 .
[51] Florent Krzakala,et al. Spectral detection on sparse hypergraphs , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[52] Emmanuel Abbe,et al. Detection in the stochastic block model with multiple clusters: proof of the achievability conjectures, acyclic BP, and the information-computation gap , 2015, ArXiv.
[53] Raj Rao Nadakuditi,et al. Graph spectra and the detectability of community structure in networks , 2012, Physical review letters.
[54] László Lovász,et al. Limits of dense graph sequences , 2004, J. Comb. Theory B.
[55] M. Mézard,et al. Analytic and Algorithmic Solution of Random Satisfiability Problems , 2002, Science.
[56] Cristopher Moore,et al. Community detection in networks with unequal groups , 2015, Physical review. E.
[57] Florent Krzakala,et al. Spectral Clustering of graphs with the Bethe Hessian , 2014, NIPS.
[58] Adel Javanmard,et al. Performance of a community detection algorithm based on semidefinite programming , 2016, ArXiv.
[59] Amin Coja-Oghlan,et al. Graph Partitioning via Adaptive Spectral Techniques , 2009, Combinatorics, Probability and Computing.
[60] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[61] Andrea Montanari,et al. The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, ISIT.
[62] Laurent Massoulié,et al. Clustering and Inference From Pairwise Comparisons , 2015, SIGMETRICS.
[63] László Lovász,et al. Large Networks and Graph Limits , 2012, Colloquium Publications.
[64] Frank McSherry,et al. Spectral partitioning of random graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.
[65] P. Wolfe,et al. Nonparametric graphon estimation , 2013, 1309.5936.
[66] Shlomo Shamai,et al. Mutual information and minimum mean-square error in Gaussian channels , 2004, IEEE Transactions on Information Theory.
[67] Jess Banks,et al. Information-theoretic thresholds for community detection in sparse networks , 2016, COLT.
[68] Elchanan Mossel,et al. A Proof of the Block Model Threshold Conjecture , 2013, Combinatorica.
[69] Audry Terras. What are zeta functions of graphs and what are they good for ? , 2005 .
[70] Elchanan Mossel,et al. Density Evolution in the Degree-correlated Stochastic Block Model , 2015, COLT.
[71] David M Blei,et al. Efficient discovery of overlapping communities in massive networks , 2013, Proceedings of the National Academy of Sciences.
[72] Elchanan Mossel,et al. Belief propagation, robust reconstruction and optimal recovery of block models , 2013, COLT.
[73] Amit Singer,et al. Decoding Binary Node Labels from Censored Edge Measurements: Phase Transition and Efficient Recovery , 2014, IEEE Transactions on Network Science and Engineering.
[74] T. Vicsek,et al. Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.
[75] Andrea Montanari,et al. Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.
[76] Elchanan Mossel,et al. The Kesten-Stigum Reconstruction Bound Is Tight for Roughly Symmetric Binary Channels , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[77] Aravindan Vijayaraghavan,et al. Learning Communities in the Presence of Errors , 2015, COLT.
[78] Varun Jog,et al. Information-theoretic bounds for exact recovery in weighted stochastic block models using the Renyi divergence , 2015, ArXiv.
[79] Mark E. J. Newman,et al. Generalized communities in networks , 2015, Physical review letters.
[80] Alexander S. Wein,et al. A semidefinite program for unbalanced multisection in the stochastic block model , 2017, 2017 International Conference on Sampling Theory and Applications (SampTA).
[81] Bruce E. Hajek,et al. Achieving exact cluster recovery threshold via semidefinite programming , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).
[82] C. E. SHANNON,et al. A mathematical theory of communication , 1948, MOCO.
[83] Andrea Montanari,et al. Information-theoretically optimal sparse PCA , 2014, 2014 IEEE International Symposium on Information Theory.
[84] Tiago P. Peixoto. Model selection and hypothesis testing for large-scale network models with overlapping groups , 2014, ArXiv.
[85] Yudong Chen,et al. Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices , 2014, J. Mach. Learn. Res..
[86] Richard M. Karp,et al. Algorithms for graph partitioning on the planted partition model , 2001, Random Struct. Algorithms.
[87] Florent Krzakala,et al. Information-theoretic thresholds from the cavity method , 2016, STOC.
[88] Amit Singer,et al. Linear inverse problems on Erdős-Rényi graphs: Information-theoretic limits and efficient recovery , 2014, 2014 IEEE International Symposium on Information Theory.
[89] Edoardo M. Airoldi,et al. A Survey of Statistical Network Models , 2009, Found. Trends Mach. Learn..
[90] Florent Krzakala,et al. Spectral detection in the censored block model , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).
[91] Ankur Moitra,et al. How robust are reconstruction thresholds for community detection? , 2015, STOC.
[92] Emmanuel Abbe,et al. Crossing the KS threshold in the stochastic block model with information theory , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[93] Leonidas J. Guibas,et al. Near-Optimal Joint Object Matching via Convex Relaxation , 2014, ICML.
[94] Lada A. Adamic,et al. The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.
[95] M E J Newman,et al. Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[96] Bruce Hajek,et al. Information limits for recovering a hidden community , 2015, 2016 IEEE International Symposium on Information Theory (ISIT).
[97] Elchanan Mossel,et al. Reconstruction and estimation in the planted partition model , 2012, Probability Theory and Related Fields.
[98] Shang-Hua Teng,et al. Spectral partitioning works: planar graphs and finite element meshes , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[99] S. Péché,et al. Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices , 2004, math/0403022.
[100] Elchanan Mossel,et al. Spectral redemption in clustering sparse networks , 2013, Proceedings of the National Academy of Sciences.
[101] Bruce E. Hajek,et al. Recovering a Hidden Community Beyond the Spectral Limit in O(|E|log*|V|) Time , 2015, ArXiv.
[102] Jitendra Malik,et al. Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[103] Alexandre Proutière,et al. Community Detection via Random and Adaptive Sampling , 2014, COLT.
[104] Robert Krauthgamer,et al. A polylogarithmic approximation of the minimum bisection , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[105] J. Ruiz,et al. On the purity of the limiting gibbs state for the Ising model on the Bethe lattice , 1995 .
[106] Uriel Feige,et al. Spectral techniques applied to sparse random graphs , 2005, Random Struct. Algorithms.
[107] V. Sós,et al. GRAPH LIMITS AND EXCHANGEABLE RANDOM GRAPHS , 2008 .
[108] Laurent Hébert-Dufresne,et al. Finite size analysis of the detectability limit of the stochastic block model , 2016, Physical review. E.
[109] Andrea Montanari,et al. Asymptotic Mutual Information for the Two-Groups Stochastic Block Model , 2015, ArXiv.
[110] R. Tibshirani,et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[111] Praneeth Netrapalli,et al. Non-Reconstructability in the Stochastic Block Model , 2014, ArXiv.
[112] References , 1971 .
[113] Thomas Bonald,et al. A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks , 2015, ALT.
[114] Alexandre Proutière,et al. Accurate Community Detection in the Stochastic Block Model via Spectral Algorithms , 2014, ArXiv.
[115] P. Bickel,et al. A nonparametric view of network models and Newman–Girvan and other modularities , 2009, Proceedings of the National Academy of Sciences.
[116] D. Aldous. Representations for partially exchangeable arrays of random variables , 1981 .
[117] Yoshiyuki Kabashima,et al. Limitations in the spectral method for graph partitioning: detectability threshold and localization of eigenvectors , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[118] Andrea J. Goldsmith,et al. Information Recovery From Pairwise Measurements , 2015, IEEE Transactions on Information Theory.
[119] Michele Leone,et al. (Un)detectable cluster structure in sparse networks. , 2007, Physical review letters.
[120] Mark E. J. Newman,et al. Stochastic blockmodels and community structure in networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[121] David P. Williamson,et al. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.
[122] Yufei Zhao,et al. An $L^p$ theory of sparse graph convergence I: Limits, sparse random graph models, and power law distributions , 2014, Transactions of the American Mathematical Society.
[123] M. Newman. Communities, modules and large-scale structure in networks , 2011, Nature Physics.
[124] Joel Friedman,et al. A proof of Alon's second eigenvalue conjecture and related problems , 2004, ArXiv.
[125] Afonso S. Bandeira,et al. Random Laplacian Matrices and Convex Relaxations , 2015, Found. Comput. Math..
[126] Laurent Massoulié,et al. Non-backtracking Spectrum of Random Graphs: Community Detection and Non-regular Ramanujan Graphs , 2014, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.
[127] Alexandre Proutière,et al. Optimal Cluster Recovery in the Labeled Stochastic Block Model , 2015, NIPS.
[128] Laurent Massoulié,et al. Community detection thresholds and the weak Ramanujan property , 2013, STOC.
[129] Ravi Kumar,et al. Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.
[130] P. Erdos,et al. On the evolution of random graphs , 1984 .
[131] Andrea Montanari,et al. The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, 2010 IEEE International Symposium on Information Theory.
[132] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[133] Santosh S. Vempala,et al. On clusterings-good, bad and spectral , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[134] Chao Gao,et al. Achieving Optimal Misclassification Proportion in Stochastic Block Models , 2015, J. Mach. Learn. Res..
[135] Edoardo M. Airoldi,et al. Mixed Membership Stochastic Blockmodels , 2007, NIPS.
[136] K. Hashimoto. Zeta functions of finite graphs and representations of p-adic groups , 1989 .
[137] Laurent Massoulié,et al. Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results , 2014, COLT.
[138] Elizaveta Levina,et al. On semidefinite relaxations for the block model , 2014, ArXiv.
[139] Bin Yu,et al. Impact of regularization on spectral clustering , 2013, 2014 Information Theory and Applications Workshop (ITA).
[140] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[141] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[142] Roman Vershynin,et al. Community detection in sparse networks via Grothendieck’s inequality , 2014, Probability Theory and Related Fields.
[143] Mark E. J. Newman,et al. An efficient and principled method for detecting communities in networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[144] M. Mézard,et al. Reconstruction on Trees and Spin Glass Transition , 2005, cond-mat/0512295.
[145] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[146] Aristotelis Tsirigos,et al. Detecting community structures in Hi-C genomic data , 2015, 2016 Annual Conference on Information Science and Systems (CISS).
[147] H. Kesten,et al. A Limit Theorem for Multidimensional Galton-Watson Processes , 1966 .
[148] S H Strogatz,et al. Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[149] Emmanuel Abbe,et al. Community detection in general stochastic block models: fundamental limits and efficient recovery algorithms , 2015, ArXiv.
[150] Michael L. Creech,et al. Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.
[151] Aidong Zhang,et al. Cluster analysis for gene expression data: a survey , 2004, IEEE Transactions on Knowledge and Data Engineering.
[152] Laurent Massoulié,et al. Community Detection in the Labelled Stochastic Block Model , 2012, ArXiv.
[153] Edoardo M. Airoldi,et al. Stochastic blockmodel approximation of a graphon: Theory and consistent estimation , 2013, NIPS.
[154] Cristopher Moore,et al. The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness , 2017, Bull. EATCS.
[155] Anup Rao,et al. Stochastic Block Model and Community Detection in Sparse Graphs: A spectral algorithm with optimal rate of recovery , 2015, COLT.
[156] S. Janson,et al. Graph limits and exchangeable random graphs , 2007, 0712.2749.
[157] Mark Newman,et al. Networks: An Introduction , 2010 .
[158] Noga Alon,et al. A Spectral Technique for Coloring Random 3-Colorable Graphs , 1997, SIAM J. Comput..
[159] Cristopher Moore,et al. Detectability thresholds and optimal algorithms for community structure in dynamic networks , 2015, ArXiv.
[160] Elchanan Mossel,et al. Robust reconstruction on trees is determined by the second eigenvalue , 2004, math/0406447.
[161] Martin E. Dyer,et al. The Solution of Some Random NP-Hard Problems in Polynomial Expected Time , 1989, J. Algorithms.
[162] T. Snijders,et al. Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure , 1997 .
[163] Frank Thomson Leighton,et al. Graph bisection algorithms with good average case behavior , 1984, Comb..
[164] Andrea Montanari,et al. Semidefinite programs on sparse random graphs and their application to community detection , 2015, STOC.
[165] Thomas J. Richardson,et al. An Introduction to the Analysis of Iterative Coding Systems , 2001 .
[166] Elchanan Mossel,et al. Information flow on trees , 2001, math/0107033.
[167] Emmanuel Abbe,et al. Exact Recovery in the Stochastic Block Model , 2014, IEEE Transactions on Information Theory.
[168] Richard M. Karp,et al. Algorithms for graph partitioning on the planted partition model , 1999, Random Struct. Algorithms.