Link Prediction in a Modified Heterogeneous Bibliographic Network

Researchers have discovered, in recent years, the advantages of modeling complex systems using heterogeneous information networks. These networks are comprised of heterogeneous sets of nodes and edges that better represent the different entities and relationships often found in the real world. Although heterogeneous networks provide a richer semantic view of the data, the added complexity makes it difficult to directly apply existing techniques that work well on homogeneous networks. In this paper, we propose a graph modification process that alters an existing heterogeneous bibliographic network into another network, with the purpose of highlighting the important relations in the bibliographic network. Several importance scores, some adopted from existing work and others defined in this work, are then used to measure the importance of links in the modified network. The link prediction problem is studied on the modified network by implementing a random walk-based algorithm on the network. The importance scores and the structure of the modified graph are used to guide a random walker towards relevant parts of the graph, i.e. towards nodes to which new links will be created in the future. The different properties of the proposed algorithm are evaluated experimentally on a real world bibliographic network, the DBLP. Results show that the proposed method outperforms the state-of-the-art supervised technique as well as various approaches based on topology and node attributes.