Abstract
Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize 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 because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.
Highlights
Network science is a field that is gaining increasing interest in many scientific communities and in particular among physicists that model the organization of complex systems
This entails that in bipartite networks we define the common neighbour index (CN) of two given nonadjacent seed nodes as the nodes that are involved in all possible quadrangular closures between these seed nodes, and the local community links (LCLs) as all the links that occur between these CNs (Fig. 1B)
Comparing the proposed LCP-based and classical models to the state-of-the-art methods we found that in general the LCP-based link predictors offer a significant improvement in many technological, social and biological bipartite networks of assorted size
Summary
Network science is a field that is gaining increasing interest in many scientific communities and in particular among physicists that model the organization of complex systems. In contrast to the existing node-neighbourhood-based approaches, a new strategic shift has been introduced recently in which the focus is no longer only on groups of common nodes and their node neighbours, and on the organization of the links between them (Cannistraci et al 2013) This theory, defined and tested only in monopartite undirected and unweighted networks, is known as the local community paradigm (LCP-theory) (Cannistraci et al 2013). The first and nontrivial step required for this extension is the definition of the concept of CN index in bipartite topologies: surprisingly, as mentioned above, a concept not yet formally defined in network theory
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