Topics at the Frontier of Statistics and Network Analysis: (Re)Visiting the Foundations

This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done.

[1]  Gérard Govaert,et al.  Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  L. Keele.  The Statistics of Causal Inference: A View from Political Methodology , 2015, Political Analysis.

[3]  E. Levina,et al.  Estimating network edge probabilities by neighborhood smoothing , 2015, 1509.08588.

[4]  Patrick J. Wolfe,et al.  Network histograms and universality of blockmodel approximation , 2013, Proceedings of the National Academy of Sciences.

[5]  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.

[6]  Matthew J. Salganik,et al.  Assessing respondent-driven sampling , 2010, Proceedings of the National Academy of Sciences.

[7]  D. Horvitz,et al.  A Generalization of Sampling Without Replacement from a Finite Universe , 1952 .

[8]  H. P. Hudson,et al.  An application of the theory of probabilities to the study of a priori pathometry.—Part I , 1917 .

[9]  Béla Bollobás,et al.  Modern Graph Theory , 2002, Graduate Texts in Mathematics.

[10]  P. Diaconis,et al.  Estimating and understanding exponential random graph models , 2011, 1102.2650.

[11]  László Lovász,et al.  Graph limits and parameter testing , 2006, STOC '06.

[12]  Ove Frank,et al.  Estimation of the Number of Connected Components in a Graph by Using a Sampled Subgraph , 1978 .

[13]  Christian Tallberg.  A BAYESIAN APPROACH TO MODELING STOCHASTIC BLOCKSTRUCTURES WITH COVARIATES , 2004 .

[14]  Aaron Clauset,et al.  Learning Latent Block Structure in Weighted Networks , 2014, J. Complex Networks.

[15]  Ove Frank,et al.  Models and Methods in Social Network Analysis: Network Sampling and Model Fitting , 2005 .

[16]  Philip S. Yu,et al.  QMSampler: Joint Sampling of Multiple Networks with Quality Guarantee , 2018, IEEE Transactions on Big Data.

[17]  T. Snijders,et al.  Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure , 1997 .

[18]  Mark S. Handcock,et al.  Center for Statistics and the Social Sciences University of Washington , 2007 .

[19]  S. Wasserman,et al.  Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp , 1996 .

[20]  P. Wolfe,et al.  Nonparametric graphon estimation , 2013, 1309.5936.

[21]  V. Sós,et al.  GRAPH LIMITS AND EXCHANGEABLE RANDOM GRAPHS , 2008 .

[22]  J. Gross,et al.  Graph Theory and Its Applications , 1998 .

[23]  Michael E. Sobel,et al.  What Do Randomized Studies of Housing Mobility Demonstrate? , 2006 .

[24]  Zack W. Almquist.  Random errors in egocentric networks , 2012, Soc. Networks.

[25]  Gabor Pataki,et al.  A Principal Component Analysis for Trees , 2008, 0810.0944.

[26]  Vincent Miele,et al.  Statistical clustering of temporal networks through a dynamic stochastic block model , 2015, 1506.07464.

[27]  M. Newman,et al.  Solution of the two-star model of a network. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  O. Kallenberg.  Probabilistic Symmetries and Invariance Principles , 2005 .

[29]  Tracy M. Sweet,et al.  Incorporating Covariates Into Stochastic Blockmodels , 2015 .

[30]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[31]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[32]  Elizabeth L. Ogburn,et al.  Causal diagrams for interference , 2014, 1403.1239.

[33]  C. Priebe,et al.  Universally consistent vertex classification for latent positions graphs , 2012, 1212.1182.

[34]  Garry Robins,et al.  Exponential random graph models for social networks: theories, methods and applications , 2012 .

[35]  Lisa G. Johnston,et al.  A Systematic Review of Published Respondent-Driven Sampling Surveys Collecting Behavioral and Biologic Data , 2016, AIDS and Behavior.

[36]  A. Rinaldo,et al.  CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS. , 2011, Annals of statistics.

[37]  Peng Wang,et al.  Recent developments in exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[38]  Charles F. Manski,et al.  Identification of Treatment Response with Social Interactions , 2013 .

[39]  P. Bickel,et al.  The method of moments and degree distributions for network models , 2011, 1202.5101.

[40]  P. Aronow,et al.  Estimating Average Causal Effects Under Interference Between Units , 2015 .

[41]  W. Dempsey,et al.  A framework for statistical network modeling , 2015, 1509.08185.

[42]  Bo Söderberg,et al.  Random graphs with hidden color. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[43]  J. Bunge,et al.  Estimating the Number of Species: A Review , 1993 .

[44]  Minas Gjoka,et al.  Walking in Facebook: A Case Study of Unbiased Sampling of OSNs , 2010, 2010 Proceedings IEEE INFOCOM.

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

[46]  Mark S Handcock,et al.  Network model‐assisted inference from respondent‐driven sampling data , 2011, Journal of the Royal Statistical Society. Series A,.

[47]  O. Frank.  Sampling and estimation in large social networks , 1978 .

[48]  Thierry Blu,et al.  Monte-Carlo Sure: A Black-Box Optimization of Regularization Parameters for General Denoising Algorithms , 2008, IEEE Transactions on Image Processing.

[49]  Béla Bollobás,et al.  Random Graphs , 1985 .

[50]  Tim Austin.  On exchangeable random variables and the statistics of large graphs and hypergraphs , 2008, 0801.1698.

[51]  Jon M. Kleinberg,et al.  Graph cluster randomization: network exposure to multiple universes , 2013, KDD.

[52]  Bo Söderberg,et al.  Properties of random graphs with hidden color. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[53]  Peter D. Hoff,et al.  Modeling homophily and stochastic equivalence in symmetric relational data , 2007, NIPS.

[54]  Alfred O. Hero,et al.  Dynamic Stochastic Blockmodels for Time-Evolving Social Networks , 2014, IEEE Journal of Selected Topics in Signal Processing.

[55]  M. Vidal,et al.  Effect of sampling on topology predictions of protein-protein interaction networks , 2005, Nature Biotechnology.

[56]  Eric D. Kolaczyk,et al.  On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks , 2014, J. Mach. Learn. Res..

[57]  Christos Faloutsos,et al.  Sampling from large graphs , 2006, KDD '06.

[58]  Cun-Hui Zhang.  Estimation of sums of random variables: Examples and information bounds , 2005, math/0602214.

[59]  F. Harary,et al.  Cluster Inference by using Transitivity Indices in Empirical Graphs , 1982 .

[60]  Mark S Handcock,et al.  7. Respondent-Driven Sampling: An Assessment of Current Methodology , 2009, Sociological methodology.

[61]  Edoardo M. Airoldi,et al.  Nonparametric estimation and testing of exchangeable graph models , 2014, AISTATS.

[62]  Mark S. Granovetter.  Network Sampling: Some First Steps , 1976, American Journal of Sociology.

[63]  Dana Ron,et al.  Property Testing , 2000 .

[64]  Walter Dempsey,et al.  Edge exchangeable models for network data , 2016, ArXiv.

[65]  Hongtu Zhu,et al.  Regression models on Riemannian symmetric spaces , 2017, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[66]  Hans-Peter Kriegel,et al.  Metropolis Algorithms for Representative Subgraph Sampling , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[67]  Bin Yu,et al.  Spectral clustering and the high-dimensional stochastic blockmodel , 2010, 1007.1684.

[68]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[69]  Edoardo M. Airoldi,et al.  A Consistent Histogram Estimator for Exchangeable Graph Models , 2014, ICML.

[70]  Elizabeth Levy Paluck,et al.  Peer pressure against prejudice: A high school field experiment examining social network change , 2011 .

[71]  Alain Celisse,et al.  Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model , 2011, 1105.3288.

[72]  M. Schweinberger.  Instability, Sensitivity, and Degeneracy of Discrete Exponential Families , 2011, Journal of the American Statistical Association.

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

[74]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek , 2005 .

[75]  Linus Bengtsson,et al.  The sensitivity of respondent‐driven sampling , 2012 .

[76]  Tyler J VanderWeele,et al.  On causal inference in the presence of interference , 2012, Statistical methods in medical research.

[77]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

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

[79]  Carey E. Priebe,et al.  A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs , 2011, 1108.2228.

[80]  Ramana Rao Kompella,et al.  Reconsidering the Foundations of Network Sampling , 2010 .

[81]  Edoardo M. Airoldi,et al.  Stochastic blockmodels with growing number of classes , 2010, Biometrika.

[82]  Eric P. Xing,et al.  Latent Space Inference of Internet-Scale Networks , 2016, J. Mach. Learn. Res..

[83]  N. Christakis,et al.  The Spread of Obesity in a Large Social Network Over 32 Years , 2007, The New England journal of medicine.

[84]  C. Manski.  Identification of Endogenous Social Effects: The Reflection Problem , 1993 .

[85]  Cristopher Moore,et al.  Accuracy and scaling phenomena in Internet mapping. , 2004, Physical review letters.

[86]  Le Song,et al.  A Multiscale Community Blockmodel for Network Exploration , 2011, AISTATS.

[87]  Reinhard Diestel,et al.  Graph Theory , 1997 .

[88]  C. Matias,et al.  Parameter identifiability in a class of random graph mixture models , 2010, 1006.0826.

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

[90]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[91]  Ron Kohavi,et al.  Online Controlled Experiments and A / B Tests , 2015 .

[92]  C. Matias,et al.  Identifiability of parameters in latent structure models with many observed variables , 2008, 0809.5032.

[93]  M. Halloran,et al.  Causal Inference in Infectious Diseases , 1995, Epidemiology.

[94]  Eric D. Kolaczyk,et al.  A Latent Eigenprobit Model with Link Uncertainty for Prediction of Protein–Protein Interactions , 2012 .

[95]  David R. Hunter,et al.  Model-Based Clustering of Large Networks , 2012, The annals of applied statistics.

[96]  Emily B. Fox,et al.  Sparse graphs using exchangeable random measures , 2014, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[97]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[98]  Hawoong Jeong,et al.  Statistical properties of sampled networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[99]  Bernard Fortin,et al.  Identification of Peer Effects through Social Networks , 2007, SSRN Electronic Journal.

[100]  Edoardo M. Airoldi,et al.  Stochastic blockmodel approximation of a graphon: Theory and consistent estimation , 2013, NIPS.

[101]  D. Rubin.  [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .

[102]  Pavel N Krivitsky,et al.  On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry. , 2011, Statistical science : a review journal of the Institute of Mathematical Statistics.

[103]  Russell Lyons,et al.  The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis , 2010, 1007.2876.

[104]  Huiling Le,et al.  Central limit theorems for Fréchet means in the space of phylogenetic trees , 2013 .

[105]  P. Rosenbaum.  Interference Between Units in Randomized Experiments , 2007 .

[106]  H. Russell Bernard,et al.  Estimation of Seroprevalence, Rape, and Homelessness in the United States Using a Social Network Approach , 1998, Evaluation review.

[107]  Ramana Rao Kompella,et al.  Network Sampling: From Static to Streaming Graphs , 2012, TKDD.

[108]  Dane Taylor,et al.  Clustering Network Layers with the Strata Multilayer Stochastic Block Model , 2015, IEEE Transactions on Network Science and Engineering.

[109]  Mark E. J. Newman,et al.  Stochastic blockmodels and community structure in networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[110]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[111]  Eric P. Xing,et al.  Graph estimation from multi-attribute data , 2012, J. Mach. Learn. Res..

[112]  Jun Li,et al.  Hypothesis Testing For Network Data in Functional Neuroimaging , 2014, 1407.5525.

[113]  Mark S Handcock,et al.  Local dependence in random graph models: characterization, properties and statistical inference , 2015, Journal of the American Statistical Association.

[114]  Edward K. Kao,et al.  Estimation of Causal Peer Influence Effects , 2013, ICML.

[115]  Krista Gile.  Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation , 2010, 1006.4837.

[116]  Matthew J. Salganik,et al.  How Many People Do You Know?: Efficiently Estimating Personal Network Size , 2010, Journal of the American Statistical Association.

[117]  C. Matias,et al.  New consistent and asymptotically normal parameter estimates for random‐graph mixture models , 2012 .

[118]  Franck Picard,et al.  A mixture model for random graphs , 2008, Stat. Comput..

[119]  Cosma Rohilla Shalizi,et al.  Homophily and Contagion Are Generically Confounded in Observational Social Network Studies , 2010, Sociological methods & research.

[120]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[121]  Olav Kallenberg,et al.  Multivariate Sampling and the Estimation Problem for Exchangeable Arrays , 1999 .

[122]  Daniel M. Roy,et al.  The Class of Random Graphs Arising from Exchangeable Random Measures , 2015, ArXiv.

[123]  Klaus Nordhausen,et al.  Statistical Analysis of Network Data with R , 2015 .

[124]  P. Solomon,et al.  Randomized Controlled Trials: Design and Implementation for Community-Based Psychosocial Interventions , 2009 .

[125]  D. Aldous.  Exchangeability and related topics , 1985 .

[126]  Arun K. Ramani,et al.  How complete are current yeast and human protein-interaction networks? , 2006, Genome Biology.

[127]  B. Söderberg.  General formalism for inhomogeneous random graphs. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[128]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[129]  I NICOLETTI,et al.  The Planning of Experiments , 1936, Rivista di clinica pediatrica.

[130]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[131]  Padhraic Smyth,et al.  Stochastic blockmodeling of relational event dynamics , 2013, AISTATS.

[132]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

[133]  Judd Harrison Michael,et al.  Labor dispute reconciliation in a forest products manufacturing facility , 1997 .

[134]  Edoardo M. Airoldi,et al.  Optimal model-assisted design of experiments for network correlated outcomes suggests new notions of network balance , 2015 .

[135]  Purnamrita Sarkar,et al.  Hypothesis testing for automated community detection in networks , 2013, ArXiv.

[136]  P. Rosenbaum,et al.  Reduced Sensitivity to Hidden Bias at Upper Quantiles in Observational Studies with Dilated Treatment Effects , 1999, Biometrics.

[137]  László Lovász,et al.  Limits of dense graph sequences , 2004, J. Comb. Theory B.

[138]  Matthew O. Jackson,et al.  Tractable and Consistent Random Graph Models , 2012, ArXiv.

[139]  Bruce D. Spencer,et al.  Estimating network degree distributions under sampling: An inverse problem, with applications to monitoring social media networks , 2013, 1305.4977.

[140]  Ove Frank,et al.  Estimation of the number of vertices of different degrees in a graph , 1980 .

[141]  Jing Lei.  A goodness-of-fit test for stochastic block models , 2014, 1412.4857.

[142]  Garry Robins,et al.  An introduction to exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[143]  László Lovász,et al.  Large Networks and Graph Limits , 2012, Colloquium Publications.

[144]  Harrison H. Zhou,et al.  Rate-optimal graphon estimation , 2014, 1410.5837.

[145]  Matthew J. Salganik,et al.  5. Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling , 2004 .

[146]  F. Chung,et al.  Complex Graphs and Networks , 2006 .

[147]  Mahendra Mariadassou,et al.  Co-clustering through Latent Bloc Model: a Review , 2015 .

[148]  W. T. Gowers,et al.  Lower bounds of tower type for Szemerédi's uniformity lemma , 1997 .

[149]  Eric D. Kolaczyk,et al.  Statistical Analysis of Network Data: Methods and Models , 2009 .

[150]  David Gold,et al.  Network‐based Auto‐probit Modeling for Protein Function Prediction , 2011, Biometrics.

[151]  Maosong Sun,et al.  Community Detection on Weighted Networks: A Variational Bayesian Method , 2009, ACML.

[152]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[153]  Daniele Durante,et al.  Nonparametric Bayes Modeling of Populations of Networks , 2014, 1406.7851.

[154]  Douglas D. Heckathorn,et al.  Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hi , 2002 .

[155]  Stephen E. Fienberg,et al.  A Brief History of Statistical Models for Network Analysis and Open Challenges , 2012 .

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

[157]  P. ERDbS.  ON THE STRENGTH OF CONNECTEDNESS OF A RANDOM GRAPH , 2001 .

[158]  Le Song,et al.  Dynamic mixed membership blockmodel for evolving networks , 2009, ICML '09.

[159]  Yonina C. Eldar.  Generalized SURE for Exponential Families: Applications to Regularization , 2008, IEEE Transactions on Signal Processing.

[160]  Carsten Wiuf,et al.  Sampling properties of random graphs: the degree distribution. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[161]  Yuchung J. Wang,et al.  Stochastic Blockmodels for Directed Graphs , 1987 .

[162]  Ji Zhu,et al.  Consistency of community detection in networks under degree-corrected stochastic block models , 2011, 1110.3854.

[163]  B. Bollobás.  The evolution of random graphs , 1984 .

[164]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[165]  P. Holland.  Statistics and Causal Inference , 1985 .

[166]  Louis J. Billera,et al.  Geometry of the Space of Phylogenetic Trees , 2001, Adv. Appl. Math..

[167]  St'ephane Robin,et al.  Uncovering latent structure in valued graphs: A variational approach , 2010, 1011.1813.

[168]  D. Watts,et al.  An Experimental Study of Search in Global Social Networks , 2003, Science.

[169]  G. Imbens,et al.  Exact p-Values for Network Interference , 2015, 1506.02084.

[170]  Alan M. Frieze,et al.  Quick Approximation to Matrices and Applications , 1999, Comb..

[171]  Béla Bollobás,et al.  Sparse graphs: Metrics and random models , 2008, Random Struct. Algorithms.

[172]  Edward R. Scheinerman,et al.  Random Dot Product Graph Models for Social Networks , 2007, WAW.

[173]  V. Sós,et al.  Convergent Sequences of Dense Graphs I: Subgraph Frequencies, Metric Properties and Testing , 2007, math/0702004.

[174]  Douglas D. Heckathorn,et al.  Respondent-driven sampling : A new approach to the study of hidden populations , 1997 .

[175]  Edoardo M. Airoldi,et al.  Confidence sets for network structure , 2011, NIPS.

[176]  Mark S Handcock,et al.  MODELING SOCIAL NETWORKS FROM SAMPLED DATA. , 2010, The annals of applied statistics.

[177]  Xiangyu Chang,et al.  Asymptotic Normality of Maximum Likelihood and its Variational Approximation for Stochastic Blockmodels , 2012, ArXiv.

[178]  Carey E. Priebe,et al.  Statistical Inference on Errorfully Observed Graphs , 2012, 1211.3601.

[179]  J. Marron,et al.  Object oriented data analysis: Sets of trees , 2007, 0711.3147.

[180]  Yihong Gong,et al.  Detecting communities and their evolutions in dynamic social networks—a Bayesian approach , 2011, Machine Learning.

[181]  Xin Lu,et al.  Respondent-driven Sampling on Directed Networks , 2012, 1201.1927.

[182]  Erik M. Volz,et al.  Probability based estimation theory for respondent driven sampling , 2008 .

[183]  Catherine Matias,et al.  MODELING HETEROGENEITY IN RANDOM GRAPHS THROUGH LATENT SPACE MODELS: A SELECTIVE REVIEW , 2014 .

[184]  D. Rubin,et al.  Causal Inference for Statistics, Social, and Biomedical Sciences: A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects , 2015 .

[185]  M. Hudgens,et al.  Toward Causal Inference With Interference , 2008, Journal of the American Statistical Association.

[186]  O. Frank.  A Survey of Statistical Methods for Graph Analysis , 1981 .

[187]  David Eppstein,et al.  Fast approximation of centrality , 2000, SODA '01.

[188]  Daniel M. Roy,et al.  Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[189]  Ulrik Brandes,et al.  Centrality Estimation in Large Networks , 2007, Int. J. Bifurc. Chaos.

[190]  Peng Xie,et al.  Sampling biases in IP topology measurements , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[191]  Dylan Walker,et al.  Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks , 2010, ICIS.

[192]  H. P. Hudson,et al.  An Application of the Theory of Probabilities to the Study of a Priori Pathometry.--Part III , 1917 .

[193]  Carsten Wiuf,et al.  Subnets of scale-free networks are not scale-free: sampling properties of networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[194]  John Guare,et al.  Six Degrees of Separation: A Play , 1990 .

[195]  Martina Morris,et al.  Advances in exponential random graph (p*) models , 2007, Soc. Networks.

[196]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[197]  Julien Brailly,et al.  Exponential Random Graph Models for Social Networks , 2014 .

[198]  N. Christakis,et al.  SUPPLEMENTARY ONLINE MATERIAL FOR: The Collective Dynamics of Smoking in a Large Social Network , 2022 .

[199]  T. Snijders,et al.  Estimation and Prediction for Stochastic Blockstructures , 2001 .

[200]  Béla Bollobás,et al.  The phase transition in inhomogeneous random graphs , 2007, Random Struct. Algorithms.