暂无分享,去创建一个
[1] Mark E. J. Newman,et al. Structural inference for uncertain networks , 2015, Physical review. E.
[2] J. Reichardt,et al. Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[3] F. Radicchi,et al. Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[4] M. Newman,et al. Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[5] M. E. J. Newman,et al. Network structure from rich but noisy data , 2017, Nature Physics.
[6] Patrick J. Wolfe,et al. Network histograms and universality of blockmodel approximation , 2013, Proceedings of the National Academy of Sciences.
[7] Thomas C.M. Lee,et al. Information and Complexity in Statistical Modeling , 2008 .
[8] Tiago P. Peixoto. Reconstructing networks with unknown and heterogeneous errors , 2018, Physical Review X.
[9] Nick S. Jones,et al. Community detection in networks with unobserved edges , 2018, ArXiv.
[10] M. Tribus,et al. Probability theory: the logic of science , 2003 .
[11] Jure Leskovec,et al. Evolution of resilience in protein interactomes across the tree of life , 2018, Proceedings of the National Academy of Sciences.
[12] Emmanuel Abbe,et al. Community detection and stochastic block models: recent developments , 2017, Found. Trends Commun. Inf. Theory.
[13] Kathryn B. Laskey,et al. Stochastic blockmodels: First steps , 1983 .
[14] Jean-Charles Delvenne,et al. Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks , 2014, IEEE Transactions on Network Science and Engineering.
[15] Tom Everitt,et al. Universal Induction and Optimisation: No Free Lunch , 2013 .
[16] Chao Yang,et al. ARPACK users' guide - solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods , 1998, Software, environments, tools.
[17] Gesine Reinert,et al. Estimating the number of communities in a network , 2016, Physical review letters.
[18] R. Guimerà,et al. Modularity from fluctuations in random graphs and complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[19] Martin Rosvall,et al. An information-theoretic framework for resolving community structure in complex networks , 2007, Proceedings of the National Academy of Sciences.
[20] F. Chung,et al. Connected Components in Random Graphs with Given Expected Degree Sequences , 2002 .
[21] Joel Nishimura,et al. Configuring Random Graph Models with Fixed Degree Sequences , 2016, SIAM Rev..
[22] Elchanan Mossel,et al. Spectral redemption in clustering sparse networks , 2013, Proceedings of the National Academy of Sciences.
[23] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..
[24] Tiago P Peixoto,et al. Parsimonious module inference in large networks. , 2012, Physical review letters.
[25] V. Traag,et al. Community detection in networks with positive and negative links. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[26] Christian Tallberg. A BAYESIAN APPROACH TO MODELING STOCHASTIC BLOCKSTRUCTURES WITH COVARIATES , 2004 .
[27] Ulrike von Luxburg,et al. A tutorial on spectral clustering , 2007, Stat. Comput..
[28] Tiago P. Peixoto. Network Reconstruction and Community Detection from Dynamics , 2019, Physical review letters.
[29] Andreas Noack,et al. Modularity clustering is force-directed layout , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[30] Tiago P. Peixoto. Bayesian Stochastic Blockmodeling , 2017, Advances in Network Clustering and Blockmodeling.
[31] Mark E. J. Newman,et al. Structure and inference in annotated networks , 2015, Nature Communications.
[32] Peter Grassberger,et al. Clustering Drives Assortativity and Community Structure in Ensembles of Networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[33] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[34] Toni Vallès-Català,et al. Consistencies and inconsistencies between model selection and link prediction in networks. , 2017, Physical review. E.
[35] Tiago P. Peixoto,et al. The graph-tool python library , 2014 .
[36] H. Akaike. A new look at the statistical model identification , 1974 .
[37] S. McGregor,et al. No Free Lunch and Algorithmic Randomness , 2006 .
[38] 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.
[39] Brian W. Kernighan,et al. An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..
[40] Cristopher Moore,et al. Phase transition in the detection of modules in sparse networks , 2011, Physical review letters.
[41] Tiago P. Peixoto. Nonparametric Bayesian inference of the microcanonical stochastic block model. , 2016, Physical review. E.
[42] Daniel B. Larremore,et al. Efficiently inferring community structure in bipartite networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[43] M. Barber. Modularity and community detection in bipartite networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[44] Tiago P. Peixoto. Revealing consensus and dissensus between network partitions , 2020, Physical Review X.
[45] Edoardo M. Airoldi,et al. Stacking models for nearly optimal link prediction in complex networks , 2019, Proceedings of the National Academy of Sciences.
[46] Mark E. J. Newman,et al. Stochastic blockmodels and community structure in networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[47] Matthew J. Streeter,et al. Two Broad Classes of Functions for Which a No Free Lunch Result Does Not Hold , 2003, GECCO.
[48] Cristopher Moore,et al. Model selection for degree-corrected block models , 2012, Journal of statistical mechanics.
[49] Chris H Wiggins,et al. Bayesian approach to network modularity. , 2007, Physical review letters.
[50] Jean-Loup Guillaume,et al. Fast unfolding of communities in large networks , 2008, 0803.0476.
[51] Renaud Lambiotte,et al. Uncovering space-independent communities in spatial networks , 2010, Proceedings of the National Academy of Sciences.
[52] R. Solé,et al. Evolving protein interaction networks through gene duplication. , 2003, Journal of theoretical biology.
[53] Marcus Hutter,et al. Open Problems in Universal Induction & Intelligence , 2009, Algorithms.
[54] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[55] Alex Arenas,et al. Analysis of the structure of complex networks at different resolution levels , 2007, physics/0703218.
[56] Florent Krzakala,et al. Statistical physics of inference: thresholds and algorithms , 2015, ArXiv.
[57] Santo Fortunato,et al. Consensus clustering in complex networks , 2012, Scientific Reports.
[58] Sergio Gómez,et al. Hierarchical Multiresolution Method to Overcome the Resolution Limit in Complex Networks , 2012, Int. J. Bifurc. Chaos.
[59] Martin Rosvall,et al. Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.
[60] Peter D. Hoff,et al. Latent Space Approaches to Social Network Analysis , 2002 .
[61] Shang-Hua Teng,et al. Spectral partitioning works: planar graphs and finite element meshes , 1996, Proceedings of 37th Conference on Foundations of Computer Science.
[62] Morten Mørup,et al. Learning latent structure in complex networks , 2009 .
[63] T. Snijders,et al. Estimation and Prediction for Stochastic Blockstructures , 2001 .
[64] P. Ronhovde,et al. Phase transitions in random Potts systems and the community detection problem: spin-glass type and dynamic perspectives , 2010, 1008.2699.
[65] Tomoji Shogenji,et al. Hume’s Problem Solved: The Optimality of Meta-Induction , 2019, International Studies in the Philosophy of Science.
[66] Danny C. Sorensen,et al. Deflation Techniques for an Implicitly Restarted Arnoldi Iteration , 1996, SIAM J. Matrix Anal. Appl..
[67] D. Wolpert,et al. No Free Lunch Theorems for Search , 1995 .
[68] 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.
[69] M. E. J. Newman,et al. Representative community divisions of networks , 2021, Communications Physics.
[70] D. Garlaschelli,et al. Community detection for correlation matrices , 2013, 1311.1924.
[71] Yuhong Yang,et al. Information Theory, Inference, and Learning Algorithms , 2005 .
[72] M. E. J. Newman,et al. Consistency of community structure in complex networks , 2019, Physical review. E.
[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] Tiago P. Peixoto. Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.
[75] P. Latouche,et al. Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood , 2015 .
[76] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[77] Tiago P. Peixoto. Hierarchical block structures and high-resolution model selection in large networks , 2013, ArXiv.
[78] Cullen Schaffer,et al. A Conservation Law for Generalization Performance , 1994, ICML.
[79] Tor Lattimore,et al. No Free Lunch versus Occam's Razor in Supervised Learning , 2011, Algorithmic Probability and Friends.
[80] George D. Montanez. Why Machine Learning Works , 2017 .
[81] Wiley India. Cmos: Circuit Design, Layout, And Simulation , 2009 .
[82] Aaron Clauset,et al. Evaluating Overfit and Underfit in Models of Network Community Structure , 2018, IEEE Transactions on Knowledge and Data Engineering.
[83] Cristopher Moore,et al. Scalable detection of statistically significant communities and hierarchies, using message passing for modularity , 2014, Proceedings of the National Academy of Sciences.
[84] Vincent A. Traag,et al. From Louvain to Leiden: guaranteeing well-connected communities , 2018, Scientific Reports.
[85] Benjamin H. Good,et al. Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[86] Santo Fortunato,et al. Community detection in networks: A user guide , 2016, ArXiv.
[87] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[88] Luca Trevisan,et al. Theory and Applications of Models of Computation , 2013, Lecture Notes in Computer Science.
[89] Tatsuro Kawamoto,et al. Algorithmic detectability threshold of the stochastic blockmodel , 2017, Physical review. E.
[90] Leto Peel,et al. The ground truth about metadata and community detection in networks , 2016, Science Advances.
[91] M. Newman. Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, Physical review. E.
[92] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..
[93] Bin Yu,et al. Spectral clustering and the high-dimensional stochastic blockmodel , 2010, 1007.1684.
[94] M. Newman,et al. Hierarchical structure and the prediction of missing links in networks , 2008, Nature.
[95] Edoardo M. Airoldi,et al. Mixed Membership Stochastic Blockmodels , 2007, NIPS.
[96] Rami Puzis,et al. Link Prediction in Highly Fractional Data Sets , 2013 .
[97] S. Bornholdt,et al. When are networks truly modular , 2006, cond-mat/0606220.
[98] Roger Guimerà,et al. Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.
[99] J. Doye,et al. Thermodynamics of Community Structure , 2006, cond-mat/0610077.
[100] Cristopher Moore,et al. The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness , 2017, Bull. EATCS.
[101] Jorma Rissanen,et al. Minimum Description Length Principle , 2010, Encyclopedia of Machine Learning.
[102] Santo Fortunato,et al. Limits of modularity maximization in community detection , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[103] Santo Fortunato,et al. Network structure, metadata and the prediction of missing nodes , 2016, ArXiv.
[104] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[105] S. Fortunato,et al. Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.
[106] Tiago P. Peixoto. Disentangling homophily, community structure and triadic closure in networks , 2021, Physical Review X.
[107] Xiao Zhang,et al. Identification of core-periphery structure in networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[108] Paul M. B. Vitányi,et al. An Introduction to Kolmogorov Complexity and Its Applications , 1993, Graduate Texts in Computer Science.
[109] 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.
[110] Béla Bollobás,et al. The phase transition in inhomogeneous random graphs , 2007, Random Struct. Algorithms.
[111] Tiago P. Peixoto,et al. Statistical inference of assortative community structures , 2020, ArXiv.
[112] Marcus Hutter,et al. On Universal Prediction and Bayesian Confirmation , 2007, Theor. Comput. Sci..
[113] Yifan Hu,et al. Efficient, High-Quality Force-Directed Graph Drawing , 2006 .
[114] M. Hastings. Community detection as an inference problem. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[115] Jean-Charles Delvenne,et al. The many facets of community detection in complex networks , 2016, Applied Network Science.
[116] R. Pastor-Satorras,et al. Class of correlated random networks with hidden variables. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[117] Martin Rosvall,et al. Estimating the resolution limit of the map equation in community detection. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.
[118] Andrea Lancichinetti,et al. Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.
[119] Abraham Lempel,et al. A universal algorithm for sequential data compression , 1977, IEEE Trans. Inf. Theory.