Zhijun Yin
Tim Weninger
Jiawei Han
ABSTRACT
With the phenomenal success of networking sites (e.g., Facebook, Twitter and LinkedIn), social networks have drawn substantial attention. On online social networking sites, link recommendation is a critical task that not only helps improve user experience but also plays an essential role in network growth. In this paper we propose several link recommendation criteria, based on both user attributes and graph structure. To discover the candidates that satisfy these criteria, link relevance is estimated using a random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influence of the attributes is leveraged in the framework as well. Besides link recommendation, our framework can also rank attributes in a social network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods based on network structure and node attribute information for link recommendation.
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Digital Library
- D. Liben-Nowell and J. M. Kleinberg. The link prediction problem for social networks. In CIKM, pp. 556--559, 2003. Google ScholarDigital Library
- H. Tong, C. Faloutsos, and J.-Y. Pan. Fast random walk with restart and its applications. In ICDM, pp. 613--622, 2006. Google ScholarDigital Library
Index Terms
- LINKREC: a unified framework for link recommendation with user attributes and graph structure
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