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.
- L. Getoor and C. P. Diehl. Link mining: a survey. SIGKDD Explorations, 7(2):3--12, 2005. Google ScholarDigital 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
Recommendations
Diversity Preference-Aware Link Recommendation for Online Social Networks
Link recommendation, such as “People You May Know” on LinkedIn, recommends links to connect unlinked online social network users. Existing link recommendation methods tend to recommend similar friends to a user but overlook the fact that different users ...
Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a ...
A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions
Link recommendation has attracted significant attention from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which ...
Enterprise Social Link Recommendation
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge ManagementMany companies have started to use Enterprise Social Networks (ESNs), such as Yammer, to facilitate collaboration and communication amongst their employees in the business context. Social link recommendation, which finds and suggests whom one wants to ...
Comments