A significance-driven framework for characterizing and finding evolving patterns of news networks

Authors:
Leiming Yan;Jinwei Wang;Jin Han;Yuxiang Wang
Affiliations:
School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China;School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China;School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China;School of Computer & Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China
Venue:
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Year:
2012

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Abstract

Social network analysis has become extremely popular in recent years. What are the most significant evolving behaviors in a social network? It is very difficult to find significant evolving behaviors from a large network in a long evolving time interval. Besides, verifying and evaluating enormous dynamic patterns extracted from a large social network by experts are also too hard to generalize well. In this work, a significance-driven framework is proposed to characterize the evolution of local topology and find dynamic patterns with evidently statistical significance for temporally varying news report networks. Two significance indices--potential index and evolving score are introduced for evaluating evolving patterns. Finally, we present a systematic analysis of one real news network, which demonstrates that the method we proposed can find the evolving characteristic and extract significant dynamic patterns from news networks.