Communicability and multipartite structures in complex networks at negative absolute temperatures

Abstract

We here present a method of clearly identifying multi-partite subgraphs in a network. The method is based on a recently introduced concept of the communicability, which very clearly identifies communities in a complex network. We here show that, while the communicability at a positive temperature is useful in identifying communities, the communicability at a negative temperature is useful in idenfitying multi-partitite subgraphs; the latter quantity between two nodes is positive when the two nodes belong to the same subgraph and is negative when not. The method is able to discover `almost' multi-partite structures, where inter-community connections vastly outweigh intracommunity connections. We illustrate the relevance of this work to real-life food web and protein-protein interaction networks.

24 pages, 5 figures

References in corpus (12)

• Community structure in social and biological networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Functional cartography of complex metabolic networks
• Community detection in complex networks using Extremal Optimization
• Communicability in complex networks
• Detecting communities in large networks
• Cycles and clustering in bipartite networks
• Spectral Measures of Bipartivity in Complex Networks
• Statistical-mechanical approach to subgraph centrality in complex networks
• On network bipartivity
• Spectral Scaling in Complex Networks

Cited by in corpus (3)

• The Physics of Communicability in Complex Networks
• Spectral entropies as information-theoretic tools for complex network comparison
• Computing the Action of Trigonometric and Hyperbolic Matrix Functions