Abstract
Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.
Highlights
Most of link prediction approaches have been proposed on monopartite networks
Inspired by the idea of manifold learning[24,25] and graph regularized Nonnegative Matrix Factorization[23], in this paper we propose an algorithm framework for link prediction in bipartite networks by combining the topological structure and the latent feature model from a new perspective
The training set ET is treated as known information, and the probe set EP is used for testing the performance of methods for link prediction
Summary
Most of link prediction approaches have been proposed on monopartite networks. The most widely used methods are the similarity-based algorithms[2,7] and the supervised learning algorithms[8]. Some approaches have been developed and we mainly classify them into three categories, projection based methods, topological structure based methods, and latent feature model. Projection based methods project the bipartite network into two monopartite networks and exploit one or both monopartite layers obtained from a bipartite network to predict new links[12,13]. These methods infer the presence of links between any two nodes, belonging to the same layer, as long as sharing at least one neighbor. It is obvious that these methods lose the original topological structure information of the bipartite network[14]
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