TD-SIGNET: COMMUNITY MINING WITH WSD BASED ON IMPLIED GRAPH STRUCTURE IN SOCIAL NETWORKS

摘要

Social networks on the web are growing dramatically in size and number. The huge popularity of sites like MySpace, Face book, and others has drawn in hundreds of millions of users, and the attention of scientists and the media. The public accessibility of web-based social networks offers great promise for researchers interested in studying the behavior of users and how to integrate social information into applications. Discovering communities from a graph structure such as the Web has become an interesting research problem recently. In this paper, comparing with the state-of-the-art authority detecting and graph partitioning methods, we propose a new model to more accurately define communities. We conduct a case study to automatically discover similar interest groups (SIGNET) with taxonomy-driven (TD-SIGNET) structure in the computer science domain from the Web along with word sense disambiguation (WSD). Experiments show that our method is very effective to generate high-quality communities with more clear structure and more tunable granularity.