A Comparative Study of Web Services Composition Networks

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📝 Original Info

  • Title: A Comparative Study of Web Services Composition Networks
  • ArXiv ID: 1305.0189
  • Date: 2013-05-02
  • Authors: Researchers from original ArXiv paper

📝 Abstract

Web services growth makes the composition process a hard task to solve. This numerous interacting elements can be adequately represented by a network. Discovery and composition can benefit from the knowledge of the network structure. In this paper, we investigate the topological properties of two models of syntactic and semantic Web services composition networks: dependency and interaction. Results show that they share a similar organization characterized by the small-world property, a heavy-tailed degree distribution and a low transitivity value. Furthermore, the networks are disassortative.

💡 Deep Analysis

Deep Dive into A Comparative Study of Web Services Composition Networks.

Web services growth makes the composition process a hard task to solve. This numerous interacting elements can be adequately represented by a network. Discovery and composition can benefit from the knowledge of the network structure. In this paper, we investigate the topological properties of two models of syntactic and semantic Web services composition networks: dependency and interaction. Results show that they share a similar organization characterized by the small-world property, a heavy-tailed degree distribution and a low transitivity value. Furthermore, the networks are disassortative.

📄 Full Content

Web services growth makes the composition process a hard task to solve. This numerous interacting elements can be adequately represented by a network. Discovery and composition can benefit from the knowledge of the network structure. In this paper, we investigate the topological properties of two models of syntactic and semantic Web services composition networks: dependency and interaction. Results show that they share a similar organization characterized by the small-world property, a heavy-tailed degree distribution and a low transitivity value. Furthermore, the networks are disassortative.

Reference

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