Statistical inference in networks: fundamental limits and efficient algorithms

[1]  Bruce E. Hajek,et al.  Computational Lower Bounds for Community Detection on Random Graphs , 2014, COLT.

[2]  E. Arias-Castro,et al.  Community Detection in Random Networks , 2013, 1302.7099.

[3]  D. Hunter MM algorithms for generalized Bradley-Terry models , 2003 .

[4]  R. Karp,et al.  Algorithms for graph partitioning on the planted partition model , 2001 .

[5]  Arun Rajkumar,et al.  A Statistical Convergence Perspective of Algorithms for Rank Aggregation from Pairwise Data , 2014, ICML.

[6]  R. Durrett Random Graph Dynamics: References , 2006 .

[7]  Devavrat Shah,et al.  Rumors in a Network: Who's the Culprit? , 2009, IEEE Transactions on Information Theory.

[8]  Raj Rao Nadakuditi,et al.  Graph spectra and the detectability of community structure in networks , 2012, Physical review letters.

[9]  Jiashun Jin,et al.  FAST COMMUNITY DETECTION BY SCORE , 2012, 1211.5803.

[10]  Ludek Kucera,et al.  Expected Complexity of Graph Partitioning Problems , 1995, Discret. Appl. Math..

[11]  E. Arias-Castro,et al.  Community Detection in Sparse Random Networks , 2013, 1308.2955.

[12]  Shang-Hua Teng,et al.  Spectral Sparsification of Graphs , 2008, SIAM J. Comput..

[13]  Nir Ailon,et al.  Breaking the Small Cluster Barrier of Graph Clustering , 2013, ICML.

[14]  Xiaodong Li,et al.  Robust and Computationally Feasible Community Detection in the Presence of Arbitrary Outlier Nodes , 2014, ArXiv.

[15]  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.

[16]  Amin Coja-Oghlan,et al.  Graph Partitioning via Adaptive Spectral Techniques , 2009, Combinatorics, Probability and Computing.

[17]  Peter J. Bickel,et al.  Pseudo-likelihood methods for community detection in large sparse networks , 2012, 1207.2340.

[18]  Frank McSherry,et al.  Spectral partitioning of random graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[19]  Philippe Rigollet,et al.  Complexity Theoretic Lower Bounds for Sparse Principal Component Detection , 2013, COLT.

[20]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[21]  Béla Bollobás,et al.  Max Cut for Random Graphs with a Planted Partition , 2004, Combinatorics, Probability and Computing.

[22]  Andrea Montanari,et al.  Finding Hidden Cliques of Size $$\sqrt{N/e}$$N/e in Nearly Linear Time , 2013, Found. Comput. Math..

[23]  S. Chatterjee,et al.  Matrix estimation by Universal Singular Value Thresholding , 2012, 1212.1247.

[24]  U. Feige,et al.  Finding and certifying a large hidden clique in a semirandom graph , 2000 .

[25]  Yi-Ching Yao,et al.  Asymptotics when the number of parameters tends to infinity in the Bradley-Terry model for paired comparisons , 1999 .

[26]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[27]  U. Feige,et al.  Spectral techniques applied to sparse random graphs , 2005 .

[28]  Yu. I. Ingster,et al.  Detection of a sparse submatrix of a high-dimensional noisy matrix , 2011, 1109.0898.

[29]  Devavrat Shah,et al.  Inferring rankings under constrained sensing , 2008, NIPS.

[30]  Laurent Massoulié,et al.  Community detection thresholds and the weak Ramanujan property , 2013, STOC.

[31]  Joel A. Tropp,et al.  User-Friendly Tail Bounds for Sums of Random Matrices , 2010, Found. Comput. Math..

[32]  Sujay Sanghavi,et al.  Clustering Sparse Graphs , 2012, NIPS.

[33]  Devdatt P. Dubhashi,et al.  Balls and bins: A study in negative dependence , 1996, Random Struct. Algorithms.

[34]  Brendan P. W. Ames Guaranteed clustering and biclustering via semidefinite programming , 2012, Mathematical Programming.

[35]  Mark Braverman,et al.  Sorting from Noisy Information , 2009, ArXiv.

[36]  Laurent Massoulié,et al.  Community Detection in the Labelled Stochastic Block Model , 2012, ArXiv.

[37]  Aditya Bhaskara,et al.  Detecting high log-densities: an O(n¼) approximation for densest k-subgraph , 2010, STOC '10.

[38]  Eli Upfal,et al.  Probability and Computing: Randomized Algorithms and Probabilistic Analysis , 2005 .

[39]  Laurent Massoulié,et al.  Reconstruction in the labeled stochastic block model , 2013, 2013 IEEE Information Theory Workshop (ITW).

[40]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[41]  Babak Hassibi,et al.  Sharp performance bounds for graph clustering via convex optimization , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[42]  Avi Wigderson,et al.  Public-key cryptography from different assumptions , 2010, STOC '10.

[43]  Yudong Chen,et al.  Clustering Partially Observed Graphs via Convex Optimization , 2011, ICML.

[44]  Robert Krauthgamer,et al.  How hard is it to approximate the best Nash equilibrium? , 2009, SODA.

[45]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[46]  G. Grimmett,et al.  On colouring random graphs , 1975 .

[47]  U. Feige,et al.  Finding hidden cliques in linear time , 2009 .

[48]  Thomas P. Hayes A large-deviation inequality for vector-valued martingales , 2003 .

[49]  Fan Chung Graham,et al.  Spectral Clustering of Graphs with General Degrees in the Extended Planted Partition Model , 2012, COLT.

[50]  John Guiver,et al.  Bayesian inference for Plackett-Luce ranking models , 2009, ICML '09.

[51]  R. Gill,et al.  Applications of the van Trees inequality : a Bayesian Cramr-Rao bound , 1995 .

[52]  Yoav Seginer,et al.  The Expected Norm of Random Matrices , 2000, Combinatorics, Probability and Computing.

[53]  Yuval Peres,et al.  Finding Hidden Cliques in Linear Time with High Probability , 2010, Combinatorics, Probability and Computing.

[54]  Emmanuel Abbe,et al.  Exact Recovery in the Stochastic Block Model , 2014, IEEE Transactions on Information Theory.

[55]  Michael I. Jordan,et al.  Computational and statistical tradeoffs via convex relaxation , 2012, Proceedings of the National Academy of Sciences.

[56]  Sivaraman Balakrishnan,et al.  Minimax Localization of Structural Information in Large Noisy Matrices , 2011, NIPS.

[57]  Avrim Blum,et al.  Correlation Clustering , 2004, Machine Learning.

[58]  Santosh S. Vempala,et al.  Spectral Algorithms , 2009, Found. Trends Theor. Comput. Sci..

[59]  Ali Jalali,et al.  Clustering using Max-norm Constrained Optimization , 2012, ICML.

[60]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[61]  R. Duncan Luce,et al.  Individual Choice Behavior , 1959 .

[62]  N. Alon,et al.  Finding a large hidden clique in a random graph , 1998 .

[63]  Tao Qin,et al.  A New Probabilistic Model for Rank Aggregation , 2010, NIPS.

[64]  David P. Williamson,et al.  Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.

[65]  Michael I. Jordan,et al.  On the Consistency of Ranking Algorithms , 2010, ICML.

[66]  David C. Parkes,et al.  Preference Elicitation For General Random Utility Models , 2013, UAI.

[67]  R. A. Bradley,et al.  Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons , 1952 .

[68]  Mark Jerrum,et al.  Large Cliques Elude the Metropolis Process , 1992, Random Struct. Algorithms.

[69]  T. Tao Topics in Random Matrix Theory , 2012 .

[70]  Ari Juels,et al.  Hiding Cliques for Cryptographic Security , 1998, SODA '98.

[71]  Kathryn B. Laskey,et al.  Stochastic blockmodels: First steps , 1983 .

[72]  David C. Parkes,et al.  Computing Parametric Ranking Models via Rank-Breaking , 2014, ICML.

[73]  P. Bickel,et al.  A nonparametric view of network models and Newman–Girvan and other modularities , 2009, Proceedings of the National Academy of Sciences.

[74]  David C. Parkes,et al.  Generalized Method-of-Moments for Rank Aggregation , 2013, NIPS.

[75]  David C. Parkes,et al.  Random Utility Theory for Social Choice , 2012, NIPS.

[76]  Anima Anandkumar,et al.  A Tensor Spectral Approach to Learning Mixed Membership Community Models , 2013, COLT.

[77]  Laurent Massoulié,et al.  Distributed user profiling via spectral methods , 2014 .

[78]  P. Rigollet,et al.  Optimal detection of sparse principal components in high dimension , 2012, 1202.5070.

[79]  B. Nadler,et al.  Do Semidefinite Relaxations Really Solve Sparse PCA , 2013 .

[80]  Yihong Wu,et al.  Computational Barriers in Minimax Submatrix Detection , 2013, ArXiv.

[81]  Noga Alon,et al.  Testing k-wise and almost k-wise independence , 2007, STOC '07.

[82]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[83]  Stephen A. Vavasis,et al.  Nuclear norm minimization for the planted clique and biclique problems , 2009, Math. Program..