The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
Over the past several years, there has been a great interest in topic detection and tracking (TDT). Recently, analyzing general research trend from the huge amount of history documents also arouses considerable attention. However, existing work on TDT mainly focuses on overall trend analysis, and is unable to address questions such as "what determines the evolution of a topic?" and "when and how does a new topic get formed?". In this paper, we propose a core group model to explain the dynamics and further segment topic development. According to the division phase and interphase in the life cycle of a core group, a topic is separated into four states, i.e. birth state, extending state, saturation state and shrinkage state. Experimental results on a real dataset show that the division of a core group brings on the generation of a new topic, and the progress of an entire topic is closely correlated to the growth of a core group during its interphase.