LINKREC: A Unified Framework for Link Recommendation with User Attributes and Graph Structure
Proceedings of the 19th International Conference on World Wide Web (WWW 2010), page 1211--1212. New York, NY, USA, ACM, (2010)DOI: 10.1145/1772690.1772879
Abstract
With the phenomenal success of networking sites (<i>e.g.</i>, 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 <i>user attributes</i> and <i>graph structure</i>. 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.
Description
LINKREC
