Statistical Network Analysis: Models, Issues, and New Directions

Edoardo M. Airoldi

ISBN 10: 3540731326 / ISBN 13: 9783540731320

Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Jul 2007, 2007

Riguardo questo articolo

Neuware -This volume was prepared to share with a larger audience the exciting ideas and work presented at an ICML 2006 workshop of the same title. Network models have a long history. Sociologists and statisticians made major advances in the 1970s and 1980s, culminating in part with a number of substantial databases and the class of exponential random graph models and related methods in the early 1990s. Physicists and computer scientists came to this domain cons- erably later, but they enriched the array of models and approaches and began to tackle much larger networks and more complex forms of data. Our goal in organ- ing the workshop was to encourage a dialog among people coming from di erent disciplinary perspectives and with di erent methods, models, and tools. Both the workshop and the editing of the proceedings was a truly colla- rative e ort on behalf of all six editors, but three in particular deserve special recognition. Anna Goldenberg and Alice Zheng were the driving force behind the entire enterprise and Edo Airoldi assisted on a number of the more important arrangements.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.

Codice articolo 9783540731320

Dati bibliografici

• Titolo: Statistical Network Analysis: Models, Issues, and New Directions
• Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Jul 2007
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 3540731326
• ISBN 13: 9783540731320
• Rilegatura: Taschenbuch
• Condizione: Neu

Descrizione articolo

Riassunto

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.

Contenuti

Invited Presentations.- Structural Inference of Hierarchies in Networks.- Heider vs Simmel: Emergent Features in Dynamic Structures.- Joint Group and Topic Discovery from Relations and Text.- Statistical Models for Networks: A Brief Review of Some Recent Research.- Other Presentations.- Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis.- Exploratory Study of a New Model for Evolving Networks.- A Latent Space Model for Rank Data.- A Simple Model for Complex Networks with Arbitrary Degree Distribution and Clustering.- Discrete Temporal Models of Social Networks.- Approximate Kalman Filters for Embedding Author-Word Co-occurrence Data over Time.- Discovering Functional Communities in Dynamical Networks.- Empirical Analysis of a Dynamic Social Network Built from PGP Keyrings.- Extended Abstracts.- A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring.- Age and Geographic Inferences of the LiveJournal Social Network.- Inferring Organizational Titles in Online Communication.- Learning Approximate MRFs from Large Transactional Data.- Panel Discussion.- Panel Discussion.