Mining competitive relationships by learning across heterogeneous networks

  • Authors:

  • Yang Yang;Jie Tang;Jacklyne Keomany;Yanting Zhao;Juanzi Li;Ying Ding;Tian Li;Liangwei Wang

  • Affiliations:

  • Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Indiana University, Bloomington, IN, USA;Tsinghua University, Beijing, China;Huawei Noah's Ark Lab, Shenzhen, China

  • Venue:
  • Proceedings of the 21st ACM international conference on Information and knowledge management
  • Year:
  • 2012

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Abstract

Detecting and monitoring competitors is fundamental to a company to stay ahead in the global market. Existing studies mainly focus on mining competitive relationships within a single data source, while competing information is usually distributed in multiple networks. How to discover the underlying patterns and utilize the heterogeneous knowledge to avoid biased aspects in this issue is a challenging problem. In this paper, we study the problem of mining competitive relationships by learning across heterogeneous networks. We use Twitter and patent records as our data sources and statistically study the patterns behind the competitive relationships. We find that the two networks exhibit different but complementary patterns of competitions. Our proposed model, Topical Factor Graph Model (TFGM), defines a latent topic layer to bridge the two networks and learns a semi-supervised learning model to classify the relationships between entities (e.g., companies or products). We test the proposed model on two real data sets and the experimental results validate the effectiveness of our model, with an average of +46\% improvement over alternative methods.