Facetnet: a framework for analyzing communities and their evolutions in dynamic networks

We discover communities from social network data, and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals' roles and social status in the network as well as changes to individuals' research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. In this novel framework, communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions. As a result, this framework...

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, Belle L. Tseng
• Keywords: Community Evolutions, Generate Evolutions, Internet Technology, Meaningful Communities, WWW 2008