Recent years have witnessed increased interests in topic detection and tracking (TDT). However, existing work mainly focuses on overall trend analysis, and is not developed for understanding the evolving process of topics. To this end, this paper aims to reveal the underlying process and reasons for topic formation and development (TFD). Along this line, based on community partitioning in social networks, a core-group model is proposed to explain the dynamics and to segment topic development. This model is inspired by the cell division mechanism in biology. Furthermore, according to the division phase and interphase in the life cycle of a core group, a topic is separated into four states including birth state, extending state, saturation state and shrinkage state. In this paper, we mainly focus our studies on scientific topic formation and development using the citation network structure among scientific papers. Experimental results on two real-world data sets show that the division of a core group brings on the generation of a new scientific topic. The results also reveal that the progress of an entire scientific topic is closely correlated to the growth of a core group during its interphase. Finally, we demonstrate the effectiveness of the proposed method in several real-life scenarios.
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Acknowledgements This research was supported in part by the NSFC Project (61272275, 61202036, 61272110, 61070011, U1135005), the 111 project(B07037), and the Open Research Fund Program of State Key Laboratory for Novel Software Technology (KFKT2011B24).
- Cell division mechanism
- Community core group
- Topic formation and development