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Tracking news article evolution by dense subgraph learning
DOI:10.1016/j.neucom.2015.05.016.png)
摘要
En 中文
As an important and challenging problem, effective knowledge discovery from news event evolution over massive news articles plays a critical role in public opinion analysis and social information security. Most existing methods for news knowledge discovery resort to news event detection based on connecting a sequence of news articles over time. However, they usually have to predetermine the temporal path length of news event evolution, which leads to the algorithmic inflexibility in practice. Moreover, they are incapable of well capturing the intrinsic contextual information among the news events, resulting in the performance degradation in noisy data. To address these issues, we propose a context-dependent news knowledge discovery method based on temporally successive news article connection using subgraph learning. The proposed method is able to adaptively construct a cross-article link network along the temporal dimension, and effectively discovers the news event pattern by dense subgraph learning using the contextual news connection structures. Based on the learning structures, we present a fast and accurate link path inference method (i.e., maximum-flow rule and minimal-connection rule). Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
News tracking
News article evolution
Dense subgraphs
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期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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