One of the most widely studied problem in mining and analysis of complex networks is the detection of community structures. The problem has been extensively studied by researchers due to its high utility and numerous applications in various domains. Many algorithmic solutions have been proposed for the community detection problem but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from a variety of domains without taking into consideration the structural and topological variations in these networks. In this paper, we evaluate the performance of different clustering algorithms as a function of varying network topology. Along with the well known LFR model to generate benchmark networks with communities,we also propose a new model named Naive Scale Free Model to study the behavior of community detection algorithms with respect to different topological features. More specifically, we are interested in the size of networks, the size of community structures, the average connectivity of nodes and the ratio of inter-intra cluster edges. Results reveal several limitations of the current popular network clustering algorithms failing to correctly find communities. This suggests the need to revisit the design of current clustering algorithms that fail to incorporate varying topological features of different networks.
Pasta @cite_18 recently proposed a tunable network generation model with community structures whose flexibility allows it to generate a variety of networks with varying structural properties. The authors focus on three structural features of community structures generated through this model, the degree distribution within each community follows power-law, high clustering coefficient of nodes within each community, and each community can be further divided into sub-communities.
@cite_18
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Recent years have seen a growing interest in the modeling and simulation of social networks to understand several social phenomena. Two important classes of networks, small world and scale free networks have gained a lot of research interest. Another important characteristic of social networks is the presence of community structures. Many social processes such as information diffusion and disease epidemics depend on the presence of community structures making it an important property for network generation models to be incorporated. In this paper, we present a tunable and growing network generation model with small world and scale free properties as well as the presence of community structures. The major contribution of this model is that the communities thus created satisfy three important structural properties: connectivity within each community follows power-law, communities have high clustering coefficient and hierarchical community structures are present in the networks generated using the proposed model. Furthermore, the model is highly robust and capable of producing networks with a number of different topological characteristics varying clustering coefficient and inter-cluster edges. Our simulation results show that the model produces small world and scale free networks along with the presence of communities depicting real world societies and social networks.