Matching-centrality decomposition and the forecasting of new links in networks

Abstract

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. First we show, for a diverse set of networks, that this decomposition provides an extremely tight fit to observed networks. Consequently, the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching-centrality decomposition can be related to this external information. Consequently, it offers a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network.

References in corpus (8)

• Cooperative Game Theory Approaches for Network Partitioning
• Hierarchical structure and the prediction of missing links in networks
• Missing and spurious interactions and the reconstruction of complex networks
• Navigability of Complex Networks
• Classes of complex networks defined by role-to-role connectivity profiles
• Efficiently inferring community structure in bipartite networks
• Predicting human preferences using the block structure of complex social networks
• Predicting future conflict between team-members with parameter-free models of social networks