Tuning degree distributions of scale-free networks

Abstract

Scale-free networks are characterized by a degree distribution with power-law behavior and have been shown to arise in many areas, ranging from the World Wide Web to transportation or social networks. Degree distributions of observed networks, however, often differ from the power-law type and data based investigations require modifications of the typical scale-free network. We present an algorithm that generates networks in which the skewness of the degree distribution is tuneable by modifying the preferential attachment step of the Barabasi-Albert construction algorithm. Skewness is linearly correlated with the maximal degree of the network and, therefore, adequately represents the influence of superspreaders or hubs. By combining our algorithm with work of Holme and Kim, we show how to generate networks with skewness gamma and clustering coefficient kappa, over a wide range of values.

13 pages, 5 figures. Submitted to Physical Review E; references added, intro rewritten

References in corpus (30)

• Emergence of scaling in random networks
• Error and attack tolerance of complex networks
• Epidemic spreading in scale-free networks
• Classes of behavior of small-world networks
• The scaling laws of human travel
• The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
• Why social networks are different from other types of networks
• The Web of Human Sexual Contacts
• Connectivity of Growing Random Networks
• Efficient Immunization Strategies for Computer Networks and Populations
• Growing Scale-Free Networks with Tunable Clustering
• Organization of Growing Random Networks
• Epidemic outbreaks in complex heterogeneous networks
• Epidemic dynamics in finite size scale-free networks
• Small-world properties of the Indian Railway network
• Halting viruses in scale-free networks
• Is the Boston subway a small-world network ?
• Properties of highly clustered networks
• Highly clustered scale-free networks
• Growing Scale-Free Networks with Small World Behavior
• A Geometric Fractal Growth Model for Scale Free Networks
• Tuning clustering in random networks with arbitrary degree distributions
• Structural transitions in scale-free networks
• Recursive graphs with small-world scale-free properties
• Random Networks with Tunable Degree Distribution and Clustering
• Evolving Apollonian Networks with Small-world Scale-free topologies
• Growing Scale-free Small-world Networks with Tunable Assortative Coefficient
• The synchronizability of highly clustered scale-free networks
• Evolving networks by merging cliques
• Evolving Scale-Free Network Model with Tunable Clustering

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• On the effect of the path length and transitivity of small-world networks on epidemic dynamics
• Coarse-graining the dynamics of network evolution: the rise and fall of a networked society