title: Stability of similarity measurements for bipartite networks.
abstract: Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.
authors.affiliation: Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, PR China.|Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.
authors.forename: Jian-Guo
authors.initials: JG
authors.lastname: Liu
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.|Informatics Research Center, Henley Business School, University of Reading, Whiteknights, RG6 6UD, United Kingdom.
authors.forename: Lei
authors.initials: L
authors.lastname: Hou
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.|Informatics Research Center, Henley Business School, University of Reading, Whiteknights, RG6 6UD, United Kingdom.
authors.forename: Xue
authors.initials: X
authors.lastname: Pan
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Guo
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou