@MISC{Zaidi_clustered, author = {Faraz Zaidi and F. Zaidi and Inria Bordeaux and Sud Ouest}, title = {Clustered}, year = {} }

Share

OpenURL

Abstract

Abstract The discovery of small world properties in real world networks has rev-olutionized the way we analyze and study real world systems. Mathematicians and Physicists in particular have closely studied and developed several models to artifi-cially generate networks with small world properties. The classical algorithms to pro-duce these graphs artificially, make use of the fact that with the introduction of some randomness in ordered graphs, small world graphs can be produced. In this paper, we present a novel algorithm to generate graphs with small world properties based on the idea that with the introduction of some order in a random graph, small world graphs can be generated. Our model starts with a randomly generate graph. We then replace each node of the random graph with cliques of different sizes. This ensures that the connectivity between the cliques is random but the clustering coefficient increases to a desired level. We further extend this model to incorporate the property of commu-nity structures (clusters) found readily in real world networks such as social, biological and technological networks. These community structures are densely connected regions of nodes in a network that are loosely connected to each other. The model generates these clustered small world graphs by replacing nodes in the random graph with densely connected set of nodes. Experimentation shows that these two models generate small world and clustered small world graphs respectively as we were able to produce the desired properties of a small world network with high clustering coefficient and low average path lengths in both cases. Furthermore, we also calculated Relative Density and Modularity to show that the clustered networks indeed had community structures.