Structural preferential attachment: Network organization beyond the link

Abstract

We introduce a mechanism which models the emergence of the universal properties of complex networks, such as scale independence, modularity and self-similarity, and unifies them under a scale-free organization beyond the link. This brings a new perspective on network organization where communities, instead of links, are the fundamental building blocks of complex systems. We show how our simple model can reproduce social and information networks by predicting their community structure and more importantly, how their nodes or communities are interconnected, often in a self-similar manner.

4 pages, 3 figures, 1 table

References in corpus (5)

• Uncovering the overlapping community structure of complex networks in nature and society
• Hierarchical structure and the prediction of missing links in networks
• Quantifying social group evolution
• Random graphs containing arbitrary distributions of subgraphs
• Propagation dynamics on networks featuring complex topologies

Cited by in corpus (16)

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• Global efficiency of local immunization on complex networks
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• Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm
• Percolation on random networks with arbitrary k-core structure
• Ising model in clustered scale-free networks
• Conflicting attachment and the growth of bipartite networks
• Structural preferential attachment: Stochastic process for the growth of scale-free, modular and self-similar systems
• Growing networks of overlapping communities with internal structure
• Complex networks as an emerging property of hierarchical preferential attachment
• Finite size analysis of the detectability limit of the stochastic block model
• Convex skeletons of complex networks
• Exact solution of bond percolation on small arbitrary graphs
• Coexistence of phases and the observability of random graphs
• A shadowing problem in the detection of overlapping communities: lifting the resolution limit through a cascading procedure
• Growing Hypergraphs with Preferential Linking