title: Predicting missing links and identifying spurious links via likelihood analysis.
abstract: Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network's probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.
authors.affiliation: Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, People's Republic of China.|Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
authors.forename: Liming
authors.initials: L
authors.lastname: Pan
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.|Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou
authors.affiliation: Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, People's Republic of China.
authors.forename: Linyuan
authors.initials: L
authors.lastname: Lü
authors.affiliation: Institute of Physics, Academia Sinica - Nankang, Taipei 11529, Taiwan.|National Center for Theoretical Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan.|Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
authors.forename: Chin-Kun
authors.initials: CK
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011336:Probability; D060756:Social Networking