IASM: An Integrated Attribute Similarity for complex networks generation

Abstract

Complex networks are seen in different real life disciplines. They are characterized by a scale-free power-law degree distribution, a small average path length (small world phenomenon), a high average clustering coefficient, and the emergence of community structure. Most proposed complex networks models did not incorporate all of the four common properties of complex networks. Models have also neglected incorporating the heterogeneous nature of network nodes. In this paper, we propose two generation models for heterogeneous complex networks. We introduce the Integrated Attribute Similarity Model (IASM). IASM uses preferential attachment to connect nodes based on their attributes similarities integrated with node's structural popularity (normalized degree or Eigen vector centrality). IASM proposed model is modified to increase their clustering coefficient using a triad formation step.