Analysis of Network Data

04 Apr 2012 13:57

---

That is, of data on 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?) Cross-validation on networks. (You could say that link prediction is leave-one-out CV, but how about k-fold CV?) Estimate a distribution over networks by somehow smoothing an adjacency matrix. — These may or may not be three aspects of a single problem.

Community discovery is an important sub-topic, and I like exponential family random graph models and graph limits enough to give them their own notebooks.

— Although many of the relevant papers appear in the journal Social Networks, published by Elsevier, the company responsible for deliberately publishing pseudo-journals such as The Australasian Journal of Bone and Joint Medicine, I know of no particular reason to believe that their findings are problematic. It would, however, be good if the community could shift to a journal whose publishers do not subvert the peer-review process whenever they find it profitable to do so.

See also: Complex networks; Community discovery; Exponential families of random graph models; Homophily vs. influence; Relational learning Social networks; Statistics in general; Statistics of structured data;

Recommended, big picture:
• 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
• Eric D. Kolaczyk, Statistical Analysis of Network Data: Methods and Models [Best available up-to-date textbook on the subject. Mini-review.]
• John Scott, Social Network Analysis: A Handbook [Short introductory text. Good on the sociology, but the implied reader is not at all comfortable with math, which can be tedious if you are.]

Recommended, close-ups:
• 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
• 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
• 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.]
• Aaron Clauset and Cristopher Moore, "Accuracy and Scaling Phenomena in Internet Mapping", cond-mat/0410059 = Physical Review Letters 94 (2005): 018701
• Aaron Clauset, Cristopher Moore and M. E. J. Newman, "Structural Inference of Hierarchies in Networks", physics/0610051
• Hoda Eldaridry and Jennifer Neville, "A Resampling Technique for Relational Data Graphs", SNA-KDD 2008 [PDF reprint via Prof. Neville]
• 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 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]
• Gueorgi Kossinets, "Effects of Missing Data in Social Networks", Social Networks 28 (2006): 247--268, arxiv:cond-mat/0306335
• Giuseppe Jurman, Samantha Riccadonna, Roberto Visintainer, Cesare Furlanello, "Biological network comparison via Ipsen-Mikhailov distance", arxiv:1109.0220 [The metric, to be honest, is not especially compelling, but it's nice to see this done at all.]
• Eric D. Kolaczyk and Pavel N. Krivitsky, "On the question of effective sample size in network modeling", arxiv:1112.0840
• Mladen Kolar, Le Song, Amr Ahmed, and Eric P. Xing, "Estimating time-varying networks", Annals of Applied Statistics 4 (2010): 94--123, arxiv:http://arxiv.org/abs/0812.5087
• 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.]
• 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.]
• Art B. Owen and Dean G. Eckles, "Bootstrapping data arrays of arbitrary order", arxiv:1106.2125
• 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
• 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]
• Michael P. H. Stumpf and Carsten Wiuf, "Sampling properties of random graphs: the degree distribution", cond-math/0507345 = Physical Review E 72 (2005): 036118
• 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
• 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
• 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 what they call "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.]

Modesty forbids me to recommend:
• CRS and Alessandro Rinaldo, "Consistency under Sampling of Exponential Random Graph Models", 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]

To read:
• Alexandre H. Abdo and A. P. S. de Moura, "Clustering as a measure of the local topology of networks", physics/0605235
• 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
• Tomaso Aste, Ruggero Gramatica, T. Di Matteo, "Exploring complex networks via topological embedding on surfaces", arxiv:1107.3456
• Yves F. Atchade, "Estimation of Network structures from partially observed Markov random fields", arxiv:1108.2835
• Pierre Baldi et al., Modeling the Internet and the Web: Probabilistic Methods and Algorithms
• Kim Baskerville and Maya Paczuski, "Subgraph ensembles and motif discovery using an alternative heuristic for graph isomorphism", Physical Review E 74 (2006): 051903
• Etienne Birmele, "Detection of network motifs by local concentration", arxiv:0904.0365
• Cristiano Bocci, Luca Chiantini, Fabio Rapallo, "Max-plus objects to study the complexity of graphs", arxiv:1111.1352
• 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
• Ulrik Brandes, Natalie Indekofer and Martin Mader, "Visualization methods for longitudinal social networks and stochastic actor-oriented modeling", Social Networks forthcoming (2011)
• Andrea Capocci, G. Caldarelli and P. De Los Rios, "Quantitative description and modeling of real networks," cond-mat/0206336
• Federica Cerina, Vincenzo De Leo, Marc Barthelemy, Alessandro Chessa, "Spatial correlations in attribute communities", arxiv:1112.3308
• Vittoria Colizza, Alessandro Flammini, M. Angeles Serrano, Alessandro Vespignani, "Detecting rich-club ordering in complex network", physics/0602134
• 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
• 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
• 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
• 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)
• 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
• 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
• Manuel Gomez-Rodriguez, Jure Leskovec, Andreas Krause, "Inferring Networks of Diffusion and Influence", arxiv:1006.0234
• 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
• 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.]
• 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
• Peter D. Hoff, "Modeling homophily and stochastic equivalence in symmetric relational data", arxiv:0711.1146
• Petter Holme, "Local symmetries in complex networks", cond-mat/0608695
• 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
• Brian Karrer and M. E. J. Newman, "Random graphs containing arbitrary distributions of subgraphs", Physical Review E 82 (2010): 066118, arxiv:1005.1659
• Eric D. Kolaczyk, David B. Chua, Marc Barthelemy, "Co-Betweenness: A Pairwise Notion of Centrality", arxiv:0709.3420
• Mladen Kolar, Eric P. Xing, "Estimating Networks With Jumps", arxiv:1012.3795
• 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.]
• Mark A. Kramer, Uri T. Eden, Sydney S. Cash, Eric D. Kolaczyk, "Network inference - with confidence - from multivariate time series", arxiv:0903.2210
• Martin Krzywinski, Inanc Birol, Steven J. M. Jones and Marco A. Marra, "Hive plots: rational approach to visualizing networks", Briefings in Bioinformatics forthcoming (2011) [But really the action is on the webpage]
• Jerome Kunegis, Ernesto W. De Luca, Sahin Albayrak, "The Link Prediction Problem in Bipartite Networks", arxiv:1006.5367
• Matthieu Latapy and Clemence Magnien, "Measuring Fundamental Properties of Real-World Complex Networks", cs.NI/0609115 [How asymptotic are we?]
• Pierre Latouche, Etienne Birmelé, and Christophe Ambroise, "Overlapping stochastic block models with application to the French political blogosphere", Annals of Applied Statistics 5 (2011): 309--336, arxiv:0910.2098
• Sang Hoon Lee, Pan-Jun Kim, and Hawoong Jeong, "Statistical properties of sampled networks", cond-mat/0505232
• Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani, "Kronecker Graphs: An Approach to Modeling Networks", arxiv:0812.4905
• Manuel Lima, Visual Complexity: Displaying Complex Networks and Data Sets
• 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
• 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]
• Ben D. MacArthur, Rubén J. Sánchez-García, James W. Anderson, "On Automorphism Groups of Networks", Discrete Applied Mathematics 156 (2008): 3525--3531, arxiv:0705.3215
• 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
• Arun S. Maiya, Tanya Y. Berger-Wolf, "Benefits of Bias: Towards Better Characterization of Network Sampling", arxiv:1109.3911
• Sebastian Moreno, Sergey Kirshner, Jennifer Neville, S.V.N. Vishwanathan, "Tied Kronecker Product Graph Models to Capture Variance in Network Populations" [PDF reprint]
• Seth A. Myers and Jure Leskovec, "On the Convexity of Latent Social Network Inference", NIPS 23 (2010) [PDF]
• 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]
• Benjamin P. Olding, Patrick J. Wolfe, "Inference for graphs and networks: Extending classical tools to modern data", arxiv:0906.4980
• Henry Pao, Glen A. Coppersmith and Carey E. Priebe, "Statistical Inference on Random Graphs: Comparative Power Analysis", Journal of Computational and Graphical Statistics forthcoming (2011)
• 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
• 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
• 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
• Pradeep Ravikumar, Martin J. Wainwright, and John D. Lafferty, "High-dimensional Ising model selection using $\ell_1$-regularized logistic regression", Annals of Statistics 38 (2010): 1287--1319, arxiv:0804.4202
• E. S. Roberts, A. C. C. Coolen, T. Schlitt, "Tailored graph ensembles as proxies or null models for real networks II: results on directed graphs", arxiv:1101.6022
• Karl Rohe, Sourav Chatterjee, Bin Yu, "Spectral clustering and the high-dimensional Stochastic Block Model", arxiv:1007.1684
• Daniel M. Romero, Chenhao Tan, Johan Ugander, "Social-Topical Affiliations: The Interplay between Structure and Popularity", arxiv:1112.1115
• Camille Roth, "Measuring Generalized Preferential Attachment in Dynamic Social Networks", nlin.AO/0507021 [Applies more generally than to social networks]
• Areejit Samal, Olivier C. Martin, "Randomizing genome-scale metabolic networks", arxiv:1012.1473
• M. Angeles Serrano, Marian Boguna, Romualdo Pastor-Satorras, "Correlations in weighted networks", cond-mat/0609029
• Mahdi Shafiei, Hugh Chipman, "Mixed-Membership Stochastic Block-Models for Transactional Networks", arxiv:1010.1437
• Srinivas Gorur Shandilya, Marc Timme, "Inferring Network Topology from Complex Dynamics", arxiv:1007.1640
• 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
• 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
• 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
• Tiziano Squartini, Diego Garlaschelli, "Analytical maximum-likelihood method to detect patterns in real networks", New Journal of Physics 13 (2011): 083001, arxiv:1103.0701
• Lionel Tabourier, Camille Roth, Jean-Philippe Cointet, "Generating constrained random graphs using multiple edge switches", arxiv:1012.3023
• Andrew C. Thomas, Hierarchical Models for Relational Data [Ph.D. thesis, Harvard statistics dept., 2009; PDF]
• 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.)]
• Sebastian Weber, Markus Porto, "Generation of arbitrarily two-point correlated random networks", arxiv:0708.4161
• Hal Whitehead, Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis [blurb]
• 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
• 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

To write:
• CRS, "Indirect Inference of Network Growth Models"
• CRS and Shawn Mankad, "Statistical Properties of Aggregated Random Graphs"
• Co-conspirators to be named later + CRS, "Smoothing Adjacency Matrices" [if we can figure out how to do it!]
• Co-conspirators to be named later + CRS, "Model-Based Detection of Changes in Network Structure"