Statistical Analysis of Network Data with R

​Introduction.- Manipulating Network Data.- Visualizing Network Data.- Descriptive Analysis of Network Graph Characteristics.- Mathematical Models for Network Graphs.- Statistical Models for Network Graphs.- Latent Network Models.- Network Topology Inference.- Modeling and Prediction of Static Network Processes.- Dynamic Network Processes.- Analysis of Network Flow Data.

Eric D. Kolaczyk is a professor of statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the Bioinformatics Program,  the Division of Systems Engineering, and the Program in Computational Neuroscience. His publications on network-based topics, beyond the development of statistical methodology and theory, include work on applications ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks of interacting proteins to the characterization of influence of groups of actors in social networks. He is an elected fellow of the American Statistical Association (ASA) and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE). 

Gábor Csárdi is a research associate at the Department of Statistics at Harvard University, Cambridge, Mass. He holds a PhD in Computer Science from Eötvös University, Hungary. His research includes applications of network analysis in biology and social sciences, bioinformatics and computational biology, and graph algorithms. He created the igraph software package in 2005 and has been one of the lead developers since then.