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Title: Common Neighbours and the Local-Community-Paradigm for Topological Link Prediction in Bipartite Networks
Citation Type: Miscellaneous
Publication Year: 2015
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Abstract: Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across the two classes. Several coupled systems at diverse scales in science, such as plant/pollinator in ecological networks or drug/target in molecular interactomes, can be approximated using bipartite network topology. Bipartite graphs emerge naturally also in many applicative domains. For instance, modelling the connections between workers and their employers, or electors and parties they vote for, are examples of affiliation networks in social analysis. Ultimately, predicting interactions between products and consumers in personal recommendation systems and market models can provide priceless information for managing cybercommerce. Surprisingly, current complex network theory presents a theoretical bottleneck: a general framework for local-based link prediction directly in the bipartite domain is missing1. Indeed, local state-of-the-art methods for link prediction do not directly exploit the inner bipartite topology2,3, but rather rely on its projection into two one-modedimension networks, an example of which is the monopartite network of consumers connected by products and the monopartite network of products connected by consumers. Unfortunately, the one-mode-projections are always less informative than the original bipartite structure4 . Here, we overcome this theoretical obstacle, and we present a formal definition of common neighbour index5 (CN) and local-community-paradigm6 (LCP) for bipartite networks. As a consequence, we are able to introduce the first nodeneighbourhood-based and LCP-based models for topological link prediction that utilizes the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks, and represent the first attempt to create a local-based formalism that allows to intuitively implement link prediction fully in the bipartite domain.
Url: http://arxiv.org/pdf/1504.07011.pdf
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Authors: Daminelli, Simone; Thomas, Josephine M.; Duran, Claudio; Cannistraci, Carl V.
Publisher: Technische Universitat Dresden
Data Collections: IPUMS USA
Topics: Other
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