Growing Scale-Free Networks with Small World Behavior

Abstract

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small world effect. While the average shortest path length increases logartihmically as in random networks, the clustering coefficient assumes a large value independent of system size. We derive expressions for the clustering coefficient in two limiting cases: random (C ~ (ln N)^2 / N) and highly clustered (C = 5/6) scale-free networks.

4 pages, 4 figures

References in corpus (3)

• Statistical mechanics of complex networks
• Lethality and centrality in protein networks
• Highly clustered scale-free networks

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