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Modeling and navigation of social information networks in metric spaces

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Abstract

We are living in a world of various kinds of social information networks with small-world and scale-free characteristics. It is still an intriguing problem for researchers to explain how and why so many obviously different networks emerge and share common intrinsic characteristics such as short diameter, higher cluster and power-law degree distribution. Most previous works studied the topology formation and information navigation of complex networks in separated models. In this paper, we propose a metric based range intersection model to explore the topology evolution and information navigation in a synthetic way. We model the network as a set of nodes in a distance metric space where each node has an ID and a range of neighbor information around its ID in the metric space. The range of a node can be seen as the local knowledge or information that the node has around its position in the metric space. The topology is formed by setting up a link between two nodes that have intersected ranges. Information navigation over the network is modeled as a greedy routing process using neighbor links and the distance metric. Different from previous models, we do not assume that nodes join the network one by one and set up link according to the degree distribution of existing nodes or distances between nodes. Range of node is the key factor determining the topology and navigation properties of a network. Moreover, as the ranges of nodes grow, the network evolves from a set of totally isolated nodes to a connected network. Thus, we can easily model the network evolutions in terms of the network size and the individual node information range using the range intersection model. A set of experiments shows that networks constructed using the range intersection model have the scale-free degree distribution, high cluster, short diameter, and high navigability properties that are owned by the real networks.

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Authors and Affiliations

  1. Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xiaoping Sun

  2. Institute of Cyber-Physical-Social Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

    Hai Zhuge

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Hai Zhuge

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  1. Xiaoping Sun
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  2. Hai Zhuge
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Correspondence to Hai Zhuge.

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Sun, X., Zhuge, H. Modeling and navigation of social information networks in metric spaces. World Wide Web 17, 649–670 (2014). https://doi.org/10.1007/s11280-012-0199-8

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  • DOI: https://doi.org/10.1007/s11280-012-0199-8

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