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.
Yin, Zhijun, Manish Gupta, Tim Weninger, and Jiawei Han. "A unified framework for link recommendation using random walks." In Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on, pp. 152-159. IEEE, 2010. [link]