Inferring social ties across heterogenous networks

It is well known that different types of social ties have essentially different influence between people. However, users in online social networks rarely categorize their contacts into “family”, “colleagues”, or “classmates”. While a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of inferring social ties over multiple heterogeneous networks. In this work, we develop a framework for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a machine learning model, which effectively improves the accuracy of inferring the type of social relationships in a target network, by borrowing knowledge from a different source network. Our empirical study on five different genres of networks validates the effectiveness of the proposed framework. For example, by leveraging info...

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Jie Tang, Tiancheng Lou, Jon M. Kleinberg
• Keywords: Data Mining, Online Social Networks, Social Ties, Target Network, WSDM 2012