Similarity index based on local paths for link prediction of complex networks.
By Linyuan Lu, Ci-Hang Jin, Tao Zhou. Physical review. E, Statistical, nonlinear, and soft matter physics, 2009
585 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/3a6eed9474995335aa2983b32cd8c35e6daa49f8
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Citations in the corpus
- Link prediction in social networks: the state-of-the-art (2014, background): “Local Path (LP): LP metric [89] makes use of information of local paths with length 2 and length 3.”
- Recommender Systems (2012, methodology): “…matrix, A n , are equal to the number of distinct paths between respective pairs of nodes, path-dependent similarity metrics can be usually written in a compact form such as (37) for the Local Path Index [150] where only paths of length two and three count and (cid:11) is a damping parameter.”
- Link Prediction in Complex Networks: A Survey (2010, methodology): “98% of node pairs share not more than two common neighbors), the common-neighbor-based algorithms are very poor, and we should try local path index [51,78] and local random walk index [79] that make use of more information.”
- Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Edge neighborhood may be compared by using the network degree, preferential attachment methods, fitness values, community structure, network hierarchy, a stochastic bloc model, a probabilistic model, or by using hypergraphs (Albert & Albert, 2004; Liben-Nowell & Kleinberg, 2007; Yan et al., 2007a; G”
- Multiscale Embedded Gene Co-expression Network Analysis (2015, methodology): “01 in the context of link prediction has been shown to perform superior to some of already established network theoretic similarity measures in many real-world cases[63, 66].”
- A Survey of Link Prediction in Complex Networks (2016, methodology): “This index is strongly based on the Katz index but it only considers a finite number of path lengths [Lü et al. 2009].”
- The science of science: from the perspective of complex systems (2017, background): “• Local Path (LP) [260,266]: SLP ij = (A(2))ij + ✏(A(3))ij, in which A is the network’s adjacency matrix and ✏ < 1 is a free parameter.”