document: 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 will discover communities that jointly maximize the fit to the observed data and the temporal evolution . Our approach relies on formulating the problem in terms of non-negative matrix factorization , where communities and their evolutions are factorized in a unified way . Then we develop an iterative algorithm , with proven low time complexity , which is guaranteed to converge to an optimal solution . We perform extensive experimental studies , on both synthetic datasets and real datasets , to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods .
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