Abstract
Many link prediction methods have been developed to infer unobserved links or predict missing links based on the observed network structure that is always incomplete and subject to interfering noise. Thus, the performance of existing methods is usually limited in that their computation depends only on input graph structures, and they do not consider external information. The effects of social influence and homophily suggest that both network structure and node attribute information should help to resolve the task of link prediction. This work proposes SASNMF, a link prediction unified framework based on non-negative matrix factorization that considers not only graph structure but also the internal and external auxiliary information, which refers to both the node attributes and the structural latent feature information extracted from the network. Furthermore, three different combinations of internal and external information are proposed and input into the framework to solve the link prediction problem. Extensive experimental results on thirteen real networks, five node attribute networks and eight non-attribute networks show that the proposed framework has competitive performance compared with benchmark methods and state-of-the-art methods, indicating the superiority of the presented algorithm.
Highlights
As a very important research direction in complex networks, link prediction is attracting a large number of researchers from different disciplines, including computer science, biology, physics and sociology, because of its wide application
We list four types of link prediction methods as the baseline methods, including five local algorithms based on the number of common neighbours between pairs of nodes (CN,Adar index (AA),RA,Salton and Jaccard), a global random walk method(ACT) and a local path method(Katz) and negative matrix factorization (NMF) method based on matrix factorization with the Frobenius norm
Link prediction based on network topology has been one of the research hotspots in the field of data mining
Summary
As a very important research direction in complex networks, link prediction is attracting a large number of researchers from different disciplines, including computer science, biology, physics and sociology, because of its wide application. It aims to infer the likelihood of the existence of a link between two nodes unconnected by means of the known structure information in the network [1,2,3]. With the development of complex network research, people have proposed many ways to predict the links for specific networks in different fields from various. Link prediction can be used to explore the evolution mechanism of the network [4,5], recommend trusted partners in business trade [6], recommend travel hotspots [7,8], mine suspects in counterterrorism networks [9,10,11], analyse criminal networks [12,13] and so on.
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