Inferring relevant social networks from interpersonal communication

Researchers increasingly use electronic communication data to construct and study large social networks, effectively inferring unobserved ties (e.g. i is connected to j) from observed communication events (e.g. i emails j). Often overlooked, however, is the impact of tie definition on the corresponding network, and in turn the relevance of the inferred network to the research question of interest. Here we study the problem of network inference and relevance for two email data sets of different size and origin. In each case, we generate a family of networks parameterized by a threshold condition on the frequency of emails exchanged between pairs of individuals. After demonstrating that different choices of the threshold correspond to dramatically different network structures, we then formulate the relevance of these networks in terms of a series of prediction tasks that depend on various network features. In general, we find: a) that prediction accuracy is maximized over a non-tr...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Munmun De Choudhury, Winter A. Mason, Jake M. Hofman, Duncan J. Watts
• Keywords: Data Sets, Emails, Internet Technology, Prediction Tasks, WWW 2010