Corpus ID: 60146323

Statistical Inference for Some Problems in Network Analysis.

  title={Statistical Inference for Some Problems in Network Analysis.},
  author={Yunpeng Zhao},
Statistical inference for some problems in network analysis by Yunpeng Zhao Co-Chairs: Elizaveta Levina and Ji Zhu Recent advances in computing and measurement technologies have led to an explosion in the amount of data that are being collected in all areas of application. Much of these data have network or graph structures, and they are common in diverse scientific areas, such as biology, computer science, sociology and so on. This dissertation makes three contributions to inference problems… Expand


Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
This paper uses the cavity method of statistical physics to obtain an asymptotically exact analysis of the phase diagram of the stochastic block model, a commonly used generative model for social and biological networks, and develops a belief propagation algorithm for inferring functional groups or communities from the topology of the network. Expand
A Survey of Statistical Network Models
An overview of the historical development of statistical network modeling is overviewed and a number of examples that have been studied in the network literature are introduced, and a subsequent discussion focuses on anumber of prominent static and dynamic network models and their interconnections. Expand
Community structure in social and biological networks
  • M. Girvan, M. Newman
  • Physics, Computer Science
  • Proceedings of the National Academy of Sciences of the United States of America
  • 2002
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability. Expand
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  • M. Newman, E. Leicht
  • Computer Science, Physics
  • Proceedings of the National Academy of Sciences
  • 2007
A general technique for detecting structural features in large-scale network data that works by dividing the nodes of a network into classes such that the members of each class have similar patterns of connection to other nodes is described. Expand
Null models for network data
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Recent developments in exponential random graph (p*) models for social networks
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Stochastic Blockmodels for Directed Graphs
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Networks: An Introduction
This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Expand
Finding and evaluating community structure in networks.
  • M. Newman, M. Girvan
  • Computer Science, Physics
  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems. Expand