document: LINKREC : a unified framework for link recommendation with user attributes and graph structure 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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