Title
Network Science: Some History, Some Perspectives (and Some Comments on Data Mining)
Speaker
Stanley Wasserman, Indiana University
Abstract
Data mining of network data often focuses on classification methods from machine learning, statistics, and pattern recognition perspectives. These techniques have been described by many, but many of these researchers are unaware of the rich history of classification and clustering techniques originating in social network analysis.
The growth of rich social media, on-line communities, and collectively produced knowledge resources has greatly increased the need for good analytic techniques for social networks. We now have the opportunity to analyze social network data at unprecedented levels of scale and temporal resolution; this has led to a growing body of research at the intersection of the computing, statistics, and the social and behavioral sciences.
This talk discusses some of the current challenges in the analysis of large-scale social network data, focusing on the inference of social processes from data. The invasion of network science by computer scientists has produced much interesting, both good and bad, research. But it begins with a discussion of the history of network science .....and continues on to a review of a new, attractive, statistical framework for network analysis.
