This paper proposes a method for learning to rank over network data. The ranking is performed with respect to a query object which can be part of the network or outside it. The ranking method makes use of the features of the nodes as well as the existing links between them. First, a neighbors-aware ranker is trained using a large margin pairwise loss function. Neighbors-aware ranker uses target neighbors scores in addition to objects' content and therefore, the scoring is consistent in every neighborhood. Then, collective inference is performed using an iterative ranking algorithm, which propagates the results of rankers over the network. By formulating link prediction as a ranking problem, the method is tested on several networks, with papers/citations and webpages/hyperlinks. The results show that the proposed algorithm, which uses both the attributes of the nodes and the structure of the links, outperforms several other methods: a content-only ranker, a link-only one, a random walk method, a relational topic model, and a method based on the weighted number of common neighbors. In addition, the propagation algorithm improves results even when the query object is not part of the network, and scales efficiently to large networks.
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