Statistical inference in networks: fundamental limits and efficient algorithms
暂无分享,去创建一个
[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..