A Unified Framework for Link Recommendation Using Random Walks

The phenomenal success of social networking sites, such as Facebook, Twitter and LinkedIn, has revolutionized the way people communicate. This paradigm has attracted the attention of researchers that wish to study the corresponding social and technological problems. Link recommendation is a critical task that not only helps increase the linkage inside the network and also improves the user experience. In an effective link recommendation algorithm it is essential to identify the factors that influence link creation. This paper enumerates several of these intuitive criteria and proposes an approach which satisfies these factors. This approach estimates link relevance by using random walk algorithm on an augmented social graph with both attribute and structure information. The global and local influences of the attributes are leveraged in the framework as well. Other than link recommendation, our framework can also rank the attributes in the network. Experiments on DBLP and IMDB data sets demonstrate that our method outperforms state-of-the-art methods for link recommendation.

[1]  Lise Getoor,et al.  Link mining: a new data mining challenge , 2003, SKDD.

[2]  Lyle H. Ungar,et al.  Statistical Relational Learning for Link Prediction , 2003 .

[3]  Padhraic Smyth,et al.  Prediction and ranking algorithms for event-based network data , 2005, SKDD.

[4]  François Fouss,et al.  Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.

[5]  Christos Faloutsos,et al.  Fast Random Walk with Restart and Its Applications , 2006, Sixth International Conference on Data Mining (ICDM'06).

[6]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[7]  Mo Chen,et al.  Clustering via Random Walk Hitting Time on Directed Graphs , 2008, AAAI.

[8]  Hong Cheng,et al.  Graph Clustering Based on Structural/Attribute Similarities , 2009, Proc. VLDB Endow..

[9]  Ben Taskar,et al.  Link Prediction in Relational Data , 2003, NIPS.

[10]  Hisashi Kashima,et al.  A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction , 2006, Sixth International Conference on Data Mining (ICDM'06).

[11]  David D. Jensen,et al.  The case for anomalous link discovery , 2005, SKDD.

[12]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[13]  Pabitra Mitra,et al.  Feature weighting in content based recommendation system using social network analysis , 2008, WWW.

[14]  Yoshihiro Yamanishi,et al.  propagation: A fast semisupervised learning algorithm for link prediction , 2009 .

[15]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[16]  Jérôme Kunegis,et al.  Learning spectral graph transformations for link prediction , 2009, ICML '09.

[17]  Ramesh R. Sarukkai,et al.  Link prediction and path analysis using Markov chains , 2000, Comput. Networks.