Ebook: Statistical Network Analysis: Models, Issues, and New Directions: ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers

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.

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 23th International Conference on Machine Learning, ICML 2006.

The 12 revised full papers and 4 invited lectures presented together with the summary of the closing panel discussion were carefully revised and selected during two rounds of reviewing and improvement from the presentations at the workshop. The papers focus on probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference. The workshop brings together statistical network modeling researchers from different communities to create and motivate novel modeling approaches, diverse applications, and new research directions.