Analysis of Network Data

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

Community discovery is an important sub-topic.

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.]
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. Blurb]
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
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, 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]
John Scott, Social Network Analysis: A Handbook [Short introductory text. Good, but heavy on the sociology and light on the math.]
S. Wasserman and K. Faust, Social Network Analysis [Deservedly, the Bible of the field. (And much of it is just as detailed, and just as boring, as the begats and the ritual prescriptions.)]

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
Aris Anagnostopoulos, Ravi Kumar and Mohammad Mahdian, "Influence and Correlation in Social Networks", in KDD 2008 [Thanks to Dr. Madian for a preprint]
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
Linton C. Freeman and Douglas R. White (2003), "Using Galois Lattices to Represent Network Data", Sociological Methodology 23: 127--146 [PDF reprint]
Diego Garlaschelli and Maria I. Loffredo, "Maximum likelihood: extracting unbiased information from complex networks", cond-mat/0609015 [This is a much-needed corrective to the physics literature, but it makes it sound as though exponential families of random graphs were invented in 2004, and they're the first ones to apply maximum likelihood to network analysis. I'm sure, however, that these are inadvertent lapses. Definitely worth reading as a first glimpse of how to do parameter estimation correctly. Thanks to Dave Feldman for pointing it out to me.]
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.]
Steve Hanneke and Eric Xing, "Discrete Temporal Models for Social Networks", in Airoldi et al. (eds.) above [Extending exponential-family random graph models to dynamic networks. A very cool paper, making me extra proud to have taught Steve stochastic processes. PDF preprint]
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]
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]
David R. Hunter and Mark S. Handcock, "Inference in curved exponential family models for networks", Journal of Computational and Graphical Statistics 15 (2006): 565--583 [PDF preprint]
Roger Th. A. J. Leenders
- Structure and Influence: Statistical Models for the Dynamics of Actor Attributes, Network Structure and Their Interdependence [Review forthcoming]
- "Modeling social influence through network autocorrelation: constructing the weight matrix", Social Networks 24 (2002): 21--47 [Basically, part of chapter 3 of his Structure and Influence --- mostly section 3.3. PDF preprint]
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.]
Jörg Reichardt and Douglas R. White, "Role models for complex networks", arxiv:0708.0958
Garry Robins, Tom Snijders, Peng Wang, Mark Handcock and Philippa Pattison, "Recent developments in exponential randomm graph (p*) models for social networks", Social Networks 29 (2007): 192--215 [PDF reprint via Prof. Snijders]
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]
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

Alexandre H. Abdo and A. P. S. de Moura, "Clustering as a measure of the local topology of networks", physics/0605235 ["... clustering coefficient ... insufficient [for] describing the local topology of very simple networks. ... an extension, the clustering profile. We show, both conceptually and through applications to well studied networks, that this measure is a more complete and robust measure of clustering. It imposes stringent constraints on theoretical growth models, specially on aspects of the network structure that play a central role in dynamics on networks. ... richer perspective [on] hierarchy, small-worlds and clusterization."]
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
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
Andrea Capocci, G. Caldarelli and P. De Los Rios, "Quantitative description and modeling of real networks," cond-mat/0206336
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
Stephen E. Fienberg, Alessandro Rinaldo and Yi Zhou, "On the Geometry of Discrete Exponential Families with Applications to Exponential Random Graph Models", arxiv:0901.0026
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
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, "Assessing degenarcy in statistical models of social networks", CSSS working paper 39 (2003)
Robert A. Hanneman and Mark Riddle, Introduction to Social Network Methods [Online textbook, looks good.]
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
Eric D. Kolaczyk, Statistical Analysis of Network Data: Methods and Models [Blurb. I'm reading this now and liking it.]
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.]
Mark A. Kramer, Uri T. Eden, Sydney S. Cash, Eric D. Kolaczyk, "Network inference - with confidence - from multivariate time series", arxiv:0903.2210
Matthieu Latapy and Clemence Magnien, "Measuring Fundamental Properties of Real-World Complex Networks", cs.NI/0609115 [How asymptotic are we?]
Sang Hoon Lee, Pan-Jun Kim, and Hawoong Jeong, "Statistical properties of sampled networks", cond-mat/0505232
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]
Yoshiharu Maeno, Yukio Ohsawa, "Node discovery problem for a social network", arxiv:0710.4975
Philippa Pattison, Algebraic Models for Social Networks [Blurb]
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
Camille Roth, "Measuring Generalized Preferential Attachment in Dynamic Social Networks", nlin.AO/0507021 [Applies more generally than to social networks]
M. Angeles Serrano, Marian Boguna, Romualdo Pastor-Satorras, "Correlations in weighted networks", cond-mat/0609029
T. A. B. Snijders, "Markov chain Monte Carlo estimation of exponential random graph models", Journal of Social Structure 2 (2002)
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
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 Thomas, Hierarchical Models for Relational Data [Ph.D. thesis, Harvard statistics dept., 2009; PDF]
Fabien Viger, Alain Barrat, Luca Dall'Asta, Cun-Hui Zhang, Eric D. Kolaczyk, "Network Inference from TraceRoute Measurements: Internet Topology `Species'", cs.NI/0510007
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]

CRS, "Homophily, Contagion, Confounding: Pick Any Three"
CRS, "Indirect Inference of Network Growth Models"
CRS and Shawn Mankad, "Statistical Properties of Aggregated Random Graphs"

Notebooks

Analysis of Network Data