Analysis of Network Data

17 Apr 2024 00:08

That is, of data in the form of networks --- I don't (as such) care about packet flow or other aspects of computer networks...

Things I wish I knew how to do: bootstrap a network, non-parametrically. (The model with a fixed degree sequence is a start, but what's the equivalent of the block bootstraps used for time series, which preserve dependence? [Update, 2017: see my paper with Alden Green.]) Cross-validation on networks. (You could say that link prediction is leave-one-out CV, but how about k-fold CV? [Update, 2016: see the papers by Chen and Lei, and (especially) by Dabbs and Junker.]) Estimate a distribution over networks by somehow smoothing an adjacency matrix. [Update, 2016: see Lawrence Wang's thesis.] Compare networks to say if they came from the same distribution [Update, 2015: See my paper with Dena Asta]. --- These may or may not be aspects of a single problem.

Community discovery is an important sub-topic, and I like exponential family random graph models, stochastic block models and graph limits enough to give them their own notebooks. Many of the entries under graphical models are about figuring out the network of interaction between random variables from patterns of dependence across those variables.

--- Although many of the relevant papers appear in the journal Social Networks, published by Elsevier, a company known to also publish advertising disguised as peer-reviewed scientific journals (e.g., The Australasian Journal of Bone and Joint Medicine), I know of no particular reason to believe that their findings are actually meretricious propaganda on behalf of a paying client. It would, however, be better if the community would shift to a journal whose publisher did not pollute the process of scientific communication whenever it was profitable to do so.

Stephen E. Fienberg, "A Brief History of Statistical Models for Network Analysis and Open Challenges", Journal of Computational and Graphical Statistics 21 (2012): 825--839
Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi, "A survey of statistical network models", Foundations and Trends in Machine Learning 2 (2009): 1--117 = arxiv:0912.5410
Abigail Z. Jacobs, Aaron Clauset, "A unified view of generative models for networks: models, methods, opportunities, and challenges", arxiv:1411.4070
Eric D. Kolaczyk, Statistical Analysis of Network Data: Methods and Models [Best available up-to-date textbook on the subject. Mini-review.]

Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella, "Reconsidering the Foundations of Network Sampling" [PDF preprint]
Edo Airoldi, David M. Blei, Stephen E. Fienberg, Anna Goldenberg, Eric P. Xing and Alice X. Zheng (eds.), Statistical Network Analysis: Models, Issues, and New Directions [Disclaimer: contains one of my papers.]
Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing, "Mixed Membership Stochastic Blockmodels", Journal of Machine Learning Research 9 (2008): 1981--2014
Sharmodeep Bhattacharyya, Peter J. Bickel, "Subsampling Bootstrap of Count Features of Networks", arxiv:1312.2645
Peter J. Bickel, Aiyou Chen, and Elizaveta Levina, "The method of moments and degree distributions for network models", Annals of Statistics 39 (2011): 38--59
Stephen P. Borgatti, Kathleen M. Carley and David Krackhardt, "On the robustness of centrality measures under conditions of imperfect data", Social Networks 28 (2006): 124--136
Peter J. Carrington, John Scott and Stanley Wasserman (eds.), Models and Methods in Social Network Analysis [Best thought of as a supplement to Wasserman and Faust, bringing it more up to date. Blurb]
Sourav Chatterjee, Persi Diaconis and Allan Sly, "Random graphs with a given degree sequence", Annals of Applied Probability 21 (2011): 1400--1435, arxiv:1005.1136 [Interesting application of the new technology of graph limits to a classic model. May not be terribly practical yet but definitely promising.]
Kehui Chen, Jing Lei, "Network Cross-Validation for Determining the Number of Communities in Network Data", arxiv:1411.1715
Aaron Clauset and Cristopher Moore, "Accuracy and Scaling Phenomena in Internet Mapping", Physical Review Letters 94 (2005): 018701, cond-mat/0410059
Aaron Clauset, Cristopher Moore and M. E. J. Newman
- "Structural Inference of Hierarchies in Networks", physics/0610051
- "Hierarchical Structure and the Prediction of Missing Links in Networks", Nature 453 (2008): 98--101, arxiv:0811.0484
Eduardo Corona, Terran Lane, Curtis Storlie, Joshua Neil, "Using Laplacian Methods, RKHS Smoothing Splines and Bayesian Estimation as a framework for Regression on Graph and Graph Related Domains" [Technical report, University of New Mexico Computer Science, 2008-06, PDF]
Beau Dabbs and Brian Junker, "Comparison of Cross-Validation Methods for Stochastic Block Models", arxiv:1605.03000
Hoda Eldaridry and Jennifer Neville, "A Resampling Technique for Relational Data Graphs", SNA-KDD 2008 [PDF reprint via Prof. Neville]
Katherine Faust and John Skvoretz, "Comparing Networks Across Space and Time, Size and Species", Sociological Methodology 32 (2002): 267--299 [Though see some remarks in my paper with Ale Rinaldo on ERGMs, linked below]
Dennis M. Feehan, Matthew J. Salganik, "Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations", arxiv:1404.4009
Linton C. Freeman and Douglas R. White (2003), "Using Galois Lattices to Represent Network Data", Sociological Methodology 23: 127--146 [PDF reprint]
Wenjie Fu, Le Song, Eric P. Xing, "A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography", arxiv:0901.0135
Krista Gile and Mark S. Handcock, "Model-based Assessment of the Impact of Missing Data on Inference for Networks" [Working Paper 66, Center for Statistics and the Social Sciences, University of Washington (2006). PDF preprint]
Steven M. Goodreau, James A. Kitts and Martina Morris, "Birds of a Feather, Or Friend of a Friend?: Using Exponential Random Graph Models to Investigate Adolescent Social Networks", Demography 46 (2009): 103--125 [In addition to the substantive findings, this is a great introduction to the "exponential-family random graph model" (ERGM) approach to modeling complex networks.]
Mark S. Handcock and Krista J. Gile, "Modeling social networks from sampled data", Annals of Applied Statistics 4 (2010): 5--25, arxiv:1010.0891
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris (eds.), "Statistical Modeling of Social Networks with 'statnet'", special volume (24) of the Journal of Statistical Software (2008) [Introduction to a whole issue on the ERGM approach]
J. A. Henderson and P. A. Robinson, "Geometric Effects on Complex Network Structure in the Cortex", Physical Review Letters 107 (2011): 018102
Peter Hoff, "Modeling homophily and stochastic equivalence in symmetric relational data", NIPS 2007, arxiv:0711.1146
Peter D. Hoff, Adrian E. Raftery and Mark S. Handcock, "Latent Space Approaches to Social Network Analysis", Journal of the American Statistical Association 97 (2002): 1090--1098 [PDF preprint]
Jake Hofman, "Large-scale social media analysis with Hadoop" [Tutorial at ICWSM 2010. The content is not really specific to social media...]
David R. Hunter, Steven M. Goodreau and Mark S. Handcock, "Goodness of Fit of Social Network Models", Journal of the American Statistical Association 103 (2008): 248--258 [PDF]
Eric D. Kolaczyk and Gábor Csárdi, Statistical Analysis of Network Data with R
Eric D. Kolaczyk and Pavel N. Krivitsky, "On the question of effective sample size in network modeling", arxiv:1112.0840
Gueorgi Kossinets, "Effects of Missing Data in Social Networks", Social Networks 28 (2006): 247--268, arxiv:cond-mat/0306335
Sang Hoon Lee, Pan-Jun Kim, and Hawoong Jeong, "Statistical properties of sampled networks", cond-mat/0505232
Youjin Lee, Elizabeth L. Ogburn, "Testing for Network and Spatial Autocorrelation", arxiv:1710.03296
James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani and Daniel M. Roy, "Random function priors for exchangeable arrays with applications to graphs and relational data", NIPS 2012
Mahendra Mariadassou, Stéphane Robin, Corinne Vacher, "Uncovering latent structure in valued graphs: A variational approach", Annals of Applied Statistics 4 (2010): 715--742, arxiv:1011.1813
Manul Middendorf, Etay Ziv and Chris Wiggins, "Inferring Network Mechanisms: The Drosophila melanogaster Protein Interaction Network", q-bio.QM/0408010 [Machine learning meets complex networks: specifically, learning decision trees to accurately classify networks by the process which grew them. Neat.]
Sebastian Moreno, Sergey Kirshner, Jennifer Neville, S.V.N. Vishwanathan, "Tied Kronecker Product Graph Models to Capture Variance in Network Populations" [PDF reprint]
M. E. J. Newman, "Analysis of weighted networks", Physical Review E 70 (2004): 056131, arxiv:cond-mat/0407503
M. E. J. Newman, Steven H. Strogatz and Duncan J. Watts, "Random graphs with arbitrary degree distributions and their applications", Physical Review E 64 (2001): 026118, cond-mat/0007235 [Though they don't quite put it this way, these methods are very naturally employed to generate surrogate network data, which keeps the degree distribution of the original but is otherwise randomized.]
Benjamin P. Olding, Patrick J. Wolfe, "Inference for graphs and networks: Extending classical tools to modern data", arxiv:0906.4980
Art B. Owen and Dean G. Eckles, "Bootstrapping data arrays of arbitrary order", Annals of Applied Statistics 6 (2012): 895--927, arxiv:1106.2125
Henry Pao, Glen A. Coppersmith and Carey E. Priebe, "Statistical Inference on Random Graphs: Comparative Power Analysis", Journal of Computational and Graphical Statistics 20 (2011): 395--416
Patrick O. Perry, Patrick J. Wolfe, "Null models for network data", arxiv:1201.5871
Gail E. Potter and Niel Hens, "A penalized likelihood approach to estimate within-household contact networks from egocentric data", Journal of the Royal Statistical Society C 62 (2013): 629--648
Jörg Reichardt and Douglas R. White, "Role models for complex networks", arxiv:0708.0958
Martin Rosvall and Carl T. Bergstrom, "Mapping Change in Large Networks", PLoS One 5 (2010): e8694
Camille Roth, "Measuring Generalized Preferential Attachment in Dynamic Social Networks", nlin.AO/0507021 [Applies more generally than to social networks]
Purnamrita Sarkar and Andrew W. Moore, "Dynamic Social Network Analysis using Latent Space Models", forthcoming in Advances in Neural Information Processing Systems 18 (NIPS 2005) [Abstract, link to PDF]
Grant Schoenebeck, "Potential Networks, Contagious Communities, and Understanding Social Network Structure", WWW 2013, arxiv:1304.1845
Jesse Shore and Benjamin Lubin, "Spectral goodness of fit for network models", Social Networks 43 (2015): 16–-27, arxiv:1407.7247
Laura M. Smith, Kristina Lerman, Cristina Garcia-Cardona, Allon G. Percus, Rumi Ghosh, "Spectral Clustering with Epidemic Diffusion", arxiv:1303.2663
Michael P. H. Stumpf and Carsten Wiuf, "Sampling properties of random graphs: the degree distribution", Physical Review E 72 (2005): 036118, cond-math/0507345
Michael P. H. Stumpf, Carsten Wiuf and Robert M. May, "Subnets of scale-free networks are not scale-free: Sampling properties of networks", PNAS 102 (2005): 4221--4224
Daniel L. Sussman, Minh Tang, Carey E. Priebe, "Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs", arxiv:1207.6745
Andrew C. Thomas, "Censoring Out-Degree Compromises Inferences of Social Network Contagion and Autocorrelation", arxiv:1008.1636
Andrew C. Thomas and Joseph K. Blitzstein, "Valued Ties Tell Fewer Lies: Why Not To Dichotomize Network Edges With Thresholds", arxiv:1101.0788
Yu-Xiang Wang, James Sharpnack, Alex Smola, Ryan J. Tibshirani, "Trend Filtering on Graphs", arxiv:1410.7690
S. Wasserman and K. Faust, Social Network Analysis [This was, for a long time, the Bible of the field. Like the Bible, it is not without value, especially if approached as a historical document, but at the same time, much of it is over-detailed, boring, and filled with prescriptions that no longer make much sense.]
Carsten Wiuf, Markus Brameier, Oskar Hagberg and Michael P. H. Stumpf, "A likelihood approach to analysis of network data", Proceedings of the National Academy of Sciences (USA) 103 (2006): 7566--7570 [My comments. Shorter: A nice piece of work, though limited to "duplication attachment" models, a limitation which is not really made clear by the abstract.]
Douglas R. White and Vincent Duquenne, eds. (1996), special issue on "Social Network and Discrete Structure Analysis", Social Networks 18: 169--318
Rongjing Xiang and Jennifer Neville, "Relational Learning with One Network: An Asymptotic Analysis", AI Stats 2011 [PDF reprint]
Yang Yang, Ira M. Longini Jr, M. Elizabeth Halloran, "A resampling-based test to detect person-to-person transmission of infectious disease", Annals of Applied Statistics 1 (2007): 211--228, arxiv:0709.0406 [Though the null they are comparing it to is one of IID disease onset times, which is, I think, only appropriate when there is no assortative mixing in the social network for traits which influence onset times for a non-contagious disease.]
Yaonan Zhang, Eric D. Kolaczyk and Bruce D. Spencer, "Estimating Network Degree Distributions Under Sampling: An Inverse Problem, with Applications to Monitoring Social Media Networks", Annals of Applied Statistics 9 (2015): 166--199, arxiv:1305.4977
Yaojia Zhu, Xiaoran Yan, Lise Getoor, Cristopher Moore, "Scalable Text and Link Analysis with Mixed-Topic Link Models", arxiv:1303.7264

Sen Pei and Hernán A. Makse, "Spreading dynamics in complex networks", Journal of Statistical Mechanics: Theory and Experiment (2013): P12002 [Only half-recommended because, despite the title and abstract, the way they define "influence" is just the number of nodes reachable from a given node. (They're using a directed graph [LiveJournal] so that's not just the size of the connected component the node is in.) No actual spreading dynamics are analyzed. (To be really precise, they define an edge between blogs 1 and 2 iff blog 1 has cited a specific post of blog 2 at least once. A chain of such links does not necessarily indicate information spreading.) It's interesting to see how this correlates with different centrality measures, but it's not quite what was promised.]

Dena Marie Asta, Geometric Approaches to Inference: Non-Euclidean Data and Networks [Ph.D. thesis, CMU Departments of Statistics and of Engineering & Public Policy, 2015]
Justin Gross, Cues and Heuristics on Capitol Hill: Relational Decision-Making in the United States Senate [Ph.D. thesis, CMU Department of Statistics, 2010]
Neil A. Spencer, Networks, Point Processes, and Networks of Point Processes [Ph.D. thesis, CMU Departments of STatistics and of Machine Learning, 2020]
Lawrence Wang, Network Comparisons using Sample Splitting [Ph.D. thesis, CMU Department of Statistics, 2016]

Dena Asta and CRS, "Geometric Network Comparison", UAI 2015, arxiv:1411.1350
Alden Green and CRS, "Bootstrapping Exchangeable Random Graphs", Electronic Journal of Statistics 16 (2022): 1058--1095, arxiv:1711.00813
Justin H. Gross, Justin H. Kirkland and CRS, "Cosponsorship in the U.S. Senate: A Multilevel Two-Mode Approach to Detecting Subtle Social Predictors of Legislative Support" [Unpublished MS.; PDF preprint via Prof. Gross]
Edward McFowland III and CRS, "Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations", Journal of the American Statistical Association 118 (2023): 707--718, arxiv:1607.06565
Neil A. Spencer and CRS, "Projective Sparse, and Learnable Latent Space Network Models", Annals of Statistics 51 (2023): 2506--2525, arxiv:1709.09702
CRS, 36-720, Statistical Network Models, fall 2016 [Course notes]
CRS and Dena Asta, "Consistency of Maximum Likelihood for Continuous-Space Network Models I", Electronic Journal of Statistics 18 (2024): 335--354, arxiv:1711.02123
CRS and Alessandro Rinaldo, "Consistency under Sampling of Exponential Random Graph Models", Annals of Statistics 41 (2013): 508--535, arxiv:1111.3054 [More]
CRS and Andrew C. Thomas, "Homophily and Contagion Are Generically Confounded in Observational Social Network Studies", Sociological Methods and Research 40 (2011): 211--239, arxiv:1004.4704 [More]
Neil Spencer and CRS, "Projective, Sparse, and Learnable Latent Position Network Models", Annals of Statistics 51 (2023): 2506--2525, arxiv:1709.09702
Christopher N. Warren, Daniel Shore, Jessica Otis, Lawrence Wang, Mike Finegold and CRS, "Six Degrees of Francis Bacon: A Statistical Method for Reconstructing Large Historical Social Networks", Digital Humanities Quarterly 10:3 (2016)
Xiaoran Yan, Jacob E. Jensen, Florent Krzakala, Cristopher Moore, CRS, Lenka Zdeborova, Pan Zhang and Yaojia Zhu, "Model Selection for Degree-corrected Block Models", arxiv:1207.3994

Alexandre H. Abdo and A. P. S. de Moura, "Clustering as a measure of the local topology of networks", physics/0605235
Aaron B. Adcock, Blair D. Sullivan, Michael W. Mahoney, "Tree decompositions and social graphs", arxiv:1411.1546
Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella, "Network Sampling: From Static to Streaming Graphs", arxiv:1211.3412
Elizabeth S. Allman, Catherine Matias, John A. Rhodes, "Parameter identifiability in a class of random graph mixture models", arxiv:1006.0826
Gerrit Ansmann and Klaus Lehnertz, "Constrained randomization of weighted networks", Physical Review E 84 (2011): 026103
Peter M. Aronow, Forrest W. Crawford, "Nonparametric Identification for Respondent-Driven Sampling", arxiv:1504.03574
Tomaso Aste, Ruggero Gramatica, T. Di Matteo, "Exploring complex networks via topological embedding on surfaces", arxiv:1107.3456
James Atwood, Don Towsley, Krista Gile, David Jensen, "Learning to Generate Networks", arxiv:1405.5868
Pierre Baldi et al., Modeling the Internet and the Web: Probabilistic Methods and Algorithms
Anirban Banerjee, "Structural distance and evolutionary relationship of networks", arxiv:0807.3185
Kim Baskerville and Maya Paczuski, "Subgraph ensembles and motif discovery using an alternative heuristic for graph isomorphism", Physical Review E 74 (2006): 051903
Christian Bauckhage, Kristian Kersting and Fabian Hadiji, "Parameterizing the Distance Distribution of Undirected Networks", UAI 2015
Yakir Berchenko, Jonathan Rosenblatt, Simon D. W. Frost, "Modeling and Analysing Respondent Driven Sampling as a Counting Process", arxiv:1304.3505
Etienne Birmele
- "Detection of network motifs by local concentration", arxiv:0904.0365
- "Detecting local network motifs", Electronic Journal of Statistics 6 (2012): 908--933
Hendrik Blockeel, Robert Brijder, "Non-Confluent NLC Graph Grammar Inference by Compressing Disjoint Subgraphs", arxiv:0901.4876
Cristiano Bocci, Luca Chiantini, Fabio Rapallo, "Max-plus objects to study the complexity of graphs", arxiv:1111.1352
Barbara Brune, Jonathan Flossdorf, Carsten Jentsch, "Goodness of fit testing based on graph functionals for homogenous Erdos Renyi graphs", arxiv:2305.10380
Andrea Capocci, G. Caldarelli and P. De Los Rios, "Quantitative description and modeling of real networks," cond-mat/0206336
Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, Nicola Santoro, "Time-Varying Graphs and Dynamic Networks", arxiv:1012.0009
Yoon-Sik Cho, Aram Galstyan, Jeff Brantingham, George Tita, "Latent Point Process Models for Spatial-Temporal Networks", arxiv:1302.2671
Federica Cerina, Vincenzo De Leo, Marc Barthelemy, Alessandro Chessa, "Spatial correlations in attribute communities", arxiv:1112.3308
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, Kevin Murphy, "Machine Learning on Graphs: A Model and Comprehensive Taxonomy", Journal of Machine Learning Research 23 (2022): 89
Krzysztof Choromanski, "Taming graph kernels with random features", arxiv:2305.00156
Munmun De Choudhury, Winter A. Mason, Jake M. Hofman and Duncan J. Watts, "Inferring Relevant Social Networks from Interpersonal Communication", WWW 2010, pp. 301--310 [On the importance of picking the right threshold to define a binary network tie]
Vittoria Colizza, Alessandro Flammini, M. Angeles Serrano, Alessandro Vespignani, "Detecting rich-club ordering in complex network", physics/0602134
Forrest W. Crawford, "The graphical structure of respondent-driven sampling", arxiv:1406.0721
Forrest W. Crawford, Jiacheng Wu, Robert Heimer, "Hidden population size estimation from respondent-driven sampling: a network approach", arxiv:1504.08349
Mihai Cucuringu, Vincent D. Blondel and Paul Van Dooren, "Extracting spatial information from networks with low-order eigenvectors", Physical Review E 87 (2013): 032803
Luciano da F. Costa, Francisco A. Rodrigues, Gonzalo Travieso and P. R. Villas Boas, "Characterization of complex networks: A survey of measurements", cond-mat/0505185
Leon Danon, Ashley P. Ford, Thomas House, Chris P. Jewell, Matt J. Keeling, Gareth O. Roberts, Joshua V. Ross, Matthew C. Vernon, "Networks and the Epidemiology of Infectious Disease", arxiv:1011.5950
Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, Nikolai Nefedov, "Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds", arxiv:1303.2221
Anton Dries, Siegfried Nijssen, "Mining Patterns in Networks using Homomorphism", arxiv:1110.3225
Daniel M. Dunlavy, Tamara G. Kolda, Evrim Acar, "Temporal Link Prediction using Matrix and Tensor Factorizations", arxiv:1005.4006
Thibault Espinasse, Jean-Michel Loubes, "A Kriging procedure for processes indexed by graphs", arxiv:1406.6592
Ernesto Estrada, "Quantifying network heterogeneity", Physical Review E 82 (2010): 066102
Jacob G. Foster, David V. Foster, Peter Grassberger and Maya Paczuski, "Link likelihoods in random networks with fixed and partially fixed degree sequence", cond-mat/0610446
Francois Fouss, Marco Saerens and Masashi Shimbo, Algorithms and Models for Network Data and Link Analysis
Kevin Françoisse, Ilkka Kivimäki, Amin Mantrach, Fabrice Rossi, Marco Saerens, "A bag-of-paths framework for network data analysis", arxiv:1302.6766
Birgitte Freiesleben de Blasio, Taral Guldahl Seierstad, Odd O. Aalen, "Frailty effects in networks: comparison and identification of individual heterogeneity versus preferential attachment in evolving networks", Journal of the Royal Statistical Society C forthcoming (2011)
Anna Gallo, Diego Garlaschelli, Renaud Lambiotte, Fabio Saracco, Tiziano Squartini, "Strong, weak or no balance? Testing structural hypotheses in real heterogeneous networks", arxiv:2303.07023
Rumi Ghosh and Kristina Lerman, "Parameterized centrality metric for network analysis", Physical Review E 83 (2011): 066118
Gourab Ghoshal, Vinko Zlatic, Guido Caldarelli, M. E. J. Newman, "Random hypergraphs and their applications", Physical Review E 79 (2009): 066118, arxiv:0903.0419
Krista J. Gile, Lisa G. Johnston, Matthew J. Salganik, "Diagnostics for Respondent-driven Sampling", arxiv:1209.6254
Reid Ginoza and Andrew Mugler, "Network motifs come in sets: Correlations in the randomization process", Physical Review E 82 (2010): 011921
Benjamin Golub and Matthew O. Jackson, "Using selection bias to explain the observed structure of Internet diffusions", Proceedings of the National Academy of Sciences (USA) 107 (2010): 10833--10836
Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine (Runting) Shi, Dawn Song, "Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)", arxiv:1112.3265
Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, Dawn Song, "Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+", arxiv:1209.0835
Palash Goyal, Emilio Ferrara, "Graph Embedding Techniques, Applications, and Performance: A Survey", Knowledge Based Systems 151 (2018): 78--94, arxiv:1705.02801
Daniel Grady, Christian Thiemann, Dirk Brockmann, "Parameter-free identification of salient features in complex networks", arxiv:1110.3864
Roger Guimera and Marta Sales-Pardo, "Missing and spurious interactions and the reconstruction of complex networks", Proceedings of the National Academy of Sciences (USA) 106 (2009): 22073--22078
Eric C. Hall, Rebecca M. Willett, "Tracking Dynamic Point Processes on Networks", IEEE Transactions on Information Theory 62 (2016), arxiv:1409.0031
Mark S. Handcock, Krista J. Gile, "On the Concept of Snowball Sampling", arxiv:1108.0301
Robert A. Hanneman and Mark Riddle, Introduction to Social Network Methods [Online textbook, looks decent.]
Zhibing He, Yunpeng Zhao, PEter Bickel, Charles Weko, Dan Cheng and Jirui Wang, "Network Inference Using the Hub Model and Variants", Journal of the American Statistical Association forthcoming (2023)
Nicholas A. Heard, David J. Weston, Kiriaki Platanioti, David J. Hand, "Bayesian anomaly detection methods for social networks", Annals of Applied Statistics 4 (2010): 645--662, arxiv:1011.1788
Petter Holme, "Local symmetries in complex networks", cond-mat/0608695
Petter Holme, Jari Saramäki, "Temporal Networks", arxiv:1108.1780
Rui Jiang, Zhidong Tu, Ting Chen and Fengzhu Sun, "Network motif identification in stochastic networks", Proceedings of the National Academy of Sciences (USA) 103 (2006): 9404--9409
Natallia Katenka and Eric D. Kolaczyk, "Inference and characterization of multi-attribute networks with application to computational biology", Annals of Applied Statistics 6 (2012): 1068--1094
Eric D. Kolaczyk, David B. Chua, Marc Barthelemy, "Co-Betweenness: A Pairwise Notion of Centrality", arxiv:0709.3420
Gueorgi Kossinets and Duncan J. Watts
- "Empirical Analysis of an Evolving Social Network", Science 311 (2006): 88--90
- "Recovery and analysis of social networks from discrete interactions" [PDF preprint]
Vassilis Kostakos, Eamonn O'Neill, Alan Penn, "Brief encounter networks", 0709.0223 [Networks defined by brief transactions, rather than persistent ties.]
Arne Kovac, Andrew D.A.C. Smith, "Regression on a Graph", Journal of Computational and Graphical Statistics 20 (2011): 432--447, arxiv:0911.1928
Jerome Kunegis, Ernesto W. De Luca, Sahin Albayrak, "The Link Prediction Problem in Bipartite Networks", arxiv:1006.5367
Aapo Kyrola, Guy Bleloch and Carlos Guestrin, "GraphChi: Large-Scale Graph Computation on Just a PC" []
Matthieu Latapy and Clemence Magnien, "Measuring Fundamental Properties of Real-World Complex Networks", cs.NI/0609115 [How asymptotic are we?]
Claire Lemercier, "Formal network methods in history: why and how?", halshs-00521527
Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani, "Kronecker Graphs: An Approach to Modeling Networks", arxiv:0812.4905
Hairong Liu, Longin Jan Latecki, Shuicheng Yan, "Revealing Cluster Structure of Graph by Path Following Replicator Dynamic", arxiv:1303.2643
Han Liu, Xi Chen, John Lafferty and Larry Wasserman, "Graph-Valued Regression", NIPS 23 (2010) [PDF], arxiv:1006.3972
Linyuan Lu, Tao Zhou, "Link Prediction in Complex Networks: A Survey", arxiv:1010.0725
Robert Lunde, "On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling", arxiv:2306.07252
Robert Lunde, Elizaveta Levina, Ji Zhu, "Conformal Prediction for Network-Assisted Regression", arxiv:2302.10095
David Lusseau, Hal Whitehead, Shane Gero, "Incorporating uncertainty into the study of animal social networks", arxiv:0903.1519 [From a quick look, nothing in this depends on animals]
Sofus A. Macskassy, Foster Provost, "Classification in Networked Data: A Toolkit and a Univariate Case Study", Journal of Machine Learning Research 8 (2007): 935--983
Yoshiharu Maeno, Yukio Ohsawa, "Node discovery problem for a social network", arxiv:0710.4975
Michael W. Mahoney, Lorenzo Orecchia, Nisheeth K. Vishnoi, "A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally", Journal of Machine Learning Research 13 (2012): 2339--2365, arxiv:0912.0681
Arun S. Maiya, Tanya Y. Berger-Wolf, "Benefits of Bias: Towards Better Characterization of Network Sampling", arxiv:1109.3911
Catherine Matias and Stéphane Robin, "Modeling heterogeneity in random graphs: a selective review", arxiv:1402.4296
Saikat Nandy, Scott H. Holan, Michael Schweinberger, "A Socio-Demographic Latent Space Approach to Spatial Data When Geography is Important but Not All-Important", arxiv:2304.03331
Sunil K. Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega, "Localized Iterative Methods for Interpolation in Graph Structured Data", arxiv:1310.2646
Sergiy Nesterko, Joseph Blitzstein, "Bias-Variance and Breadth-Depth Tradeoffs in Respondent-Driven Sampling", arxiv:1210.6059
A. Martina Neuman, Jason J. Bramburger, "Transferability of Graph Neural Networks using Graphon and Sampling Theories", arxiv:2307.13206
Jennifer Neville, Brian Gallaghr, Tina Eliassi-Rad and Tao Wang, "Correcting evaluation bias of relational classifiers with network cross validation", Knowledge and Information Systems online before print (2011) [Open access]
M. E. J. Newman, "Estimating network structure from unreliable measurements", Physical Review E 98 (2018): 062321, arxiv:1803.02427
Sarah Ouadah, Stéphane Robin, Pierre Latouche, "A degree-based goodness-of-fit test for heterogeneous random graph models", arxiv:1507.08140
Patrick O. Perry, Patrick J. Wolfe, "Point process modeling for directed interaction networks", arxiv:1011.1703
Leonid Peshkin, "Structure induction by lossless graph compression", cs.DS/0703132
Pedro C. Pinto, Patrick Thiran, Martin Vetterli, "Locating the Source of Diffusion in Large-Scale Networks", Physical Review Letters 109 (2012): 068702, arxiv:1208.2534
Art F. Y. Poon, Kimberly C. Brouwer, Stefannie A. Strathdee, Michelle Firestone-Cruz, Remedios M. Lozada, Sergei L. Kosakovsky Pond, Douglas D. Heckathorn, Simon D. W. Frost, "Parsing Social Network Survey Data from Hidden Populations Using Stochastic Context-Free Grammars", PLoS One 4 (2009): 6777
Karthikeyan Rajendran, Ioannis G. Kevrekidis, "Analysis of data in the form of graphs", arxiv:1306.3524
Mathias Raschke, Markus Schlapfer and Roberto Nibali, "Measuring degree-degree association in networks", arxiv:1003.1634
Mathias Raschke, Markus Schlapfer and Konstantinous Trantopoulos, "Copula-based generation of degree-associated networks", arxiv:1012.0201
Emile Richard, Pierre-Andre Savalle, Nicolas Vayatis, "Graph Prediction in a Low-Rank and Autoregressive Setting", arxiv:1205.1406
Sebastien Roch and Karl Rohe, "Generalized least squares can overcome the critical threshold in respondent-driven sampling", Proceedings of the National Academy of Sciences 115 (2018): 1029--10304
Daniel M. Romero, Chenhao Tan, Johan Ugander, "Social-Topical Affiliations: The Interplay between Structure and Popularity", arxiv:1112.1115
Mauricio Sadinle, "The Strength of Arcs and Edges in Interaction Networks: Elements of a Model-Based Approach", arxiv:1211.0312
Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
- "Non-parametric Link Prediction", arxiv:1109.1077
- "Nonparametric Link Prediction in Dynamic Networks", arxiv:1206.6394
Areejit Samal, Olivier C. Martin, "Randomizing genome-scale metabolic networks", arxiv:1012.1473
Michael Schweinberger, "Statistical modelling of network panel data: Goodness of fit", British Journal of Mathematical and Statistical Psychology 65 (2012): 263--281
M. Angeles Serrano, Marian Boguna, Romualdo Pastor-Satorras, "Correlations in weighted networks", cond-mat/0609029
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt, "Weisfeiler-Lehman Graph Kernels", Journal of Machine Learning Research 12 (2011): 2539--2561
Donghyuk Shin, Si Si, Inderjit S. Dhillon, "Multi-Scale Link Prediction", arxiv:1206.1891
Mile Sikic, Alen Lancic, Nino Antulov-Fantulin, Hrvoje Stefancic, "Epidemic centrality and the underestimated epidemic impact on network peripheral nodes", arxiv:1110.2558
Aarti Singh, Robert D. Nowak, Robert Calderbank, "Detecting Weak but Hierarchically-Structured Patterns in Networks", Journal of Machine Learning Research proceedings 9 (2010): 749--756, arxiv:1003.0205
Jeffrey A. Smith, "Macrostructure from Microstructure: Generating Whole Systems from Ego Networks", Sociological Methodology 42 (2012): 155--205
Kyungchul Song, "Measuring the Graph Concordance of Locally Dependent Observations", Review of Economics and Statistics 100 (2018): 535--549, arxiv:1504.03712
Michael P. H. Stumpf, P. J. Ingram, I. Nouvel and Carsten Wiuf, "Statistical model selection methods applied to biological networks", Transactions in Computational Systems Biology forthcoming (2005) = q-bio.MN/0506013
Andrew C. Thomas, Hierarchical Models for Relational Data [Ph.D. thesis, Harvard statistics dept., 2009; PDF]
Johan Ugander, Lars Backstrom, Jon Kleinberg, "Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collections", arxiv:1304.1548
Mark J. van der Laan, "Causal Inference for Networks", UC Berkeley Biostatistics working paper no. 300 (2012)
Ashton M. Verdery, Ted Mouw, Shawn Bauldry, Peter J. Mucha, "Network Structure and Biased Variance Estimation in Respondent Driven Sampling", arxiv:1309.5109
S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt, "Graph Kernels", Journal of Machine Learning Research 11 (2010): 1201--1242 ["Graphs become ever so much easier to understand when you project them into a Hilbert space." (Not an actual quote.)]
Hal Whitehead, Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis
Yanghua Xiao, Ben D. MacArthur, Hui Wang, Momiao Xiong, and Wei Wang, "Network quotients: Structural skeletons of complex systems", Physical Review E 78 (2008): 046102
Ya Xu, Justin S. Dyer, Art B. Owen, "Empirical stationary correlations for semi-supervised learning on graphs", Annals of Applied Statistics 4 (2010): 589--614, arxiv:1011.1766
Hao Yan, Keith Levin, "Minimax rates for latent position estimation in the generalized random dot product graph", arxiv:2307.01942
Jean-Gabriel Young, Alec Kirkley, M. E. J. Newman, "Clustering of heterogeneous populations of networks", arxiv:2107.07489
Hyokun Yun, S. V. N. Vishwanathan, "Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs", arxiv:1110.5383
Hugo Zanghi, Franck Picard, Vincent Miele, and Christophe Ambroise, "Strategies for online inference of model-based clustering in large and growing networks", Annals of Applied Statistics 4 (2010): 687--714
An Zeng, Giulio Cimini, "Removing spurious interactions in complex networks", arxiv:1110.5186
Shuheng Zhou, John Lafferty, Larry Wasserman, "Time Varying Undirected Graphs", arxiv:0802.2758
Xinyi Zheng, Ryan A. Rossi, Nesreen Ahmed, Dominik Moritz, "Network Report: A Structured Description for Network Datasets", arxiv:2206.03635
Jun Zhu, "Max-Margin Nonparametric Latent Feature Models for Link Prediction", arxiv:1206.4659

Dena Asta and CRS, "Nonparametric Network Modeling in Hyperbolic Space"
CRS, "Indirect Inference of Neftwork Growth Models"
CRS and Shawn Mankad, "Statistical Properties of Aggregated Random Graphs"
Co-conspirators to be named later + CRS, "Smoothing Adjacency Matrices"
Co-conspirators to be named later + CRS, "Network Comparisons"
Co-conspirators to be named later + CRS, "Fractal Network Asymptotics"