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
2087897009 2952731778 2098106850 2079149066
In order to describe the community structure upon the dynamical evolution of complex networks, we propose a new community based evolving network (CBEN) model having increasing communities with preferential mechanisms of both community sizes and node degrees, whose cumulative distribution and raw distribution follow scale-invariant power-law distributions P(S⩾s)∼s−ν and P(k)∼k−γ with exponents of ν⩾1 and γ∈[2, +∞), respectively. Besides, complex networks generated by the CBEN model are hierarchically structured, which cover the range from disassortative networks to assortative networks. 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. Many social networks share two generic distinct features: power law distributions of degrees and a high clustering. In some cases, it is difficult to obtain the structure information of real networks. Network generators provide a way to generate test networks for simulation. We present a random network generator to generate test networks with prescribed power law distributions of degrees and a finely tunable average clustering coefficient. The generator is composed of three steps. First, the degree sequences are generated following the given degree power law exponents. Second, the generator constructs a test network with these degree sequences. Third, the test network is modified to meet the prescribed average clustering coefficient as closely as possible. Experiments show the impact of the clustering coefficient on network connectivity using this generator. The comparison with existing random network generators is presented. Four major factors govern the intricacies of community extraction in networks: (1) the literature offers a multitude of disparate community detection algorithms whose output exhibits high structural variability across the collection, (2) communities identified by algorithms may differ structurally from real communities that arise in practice, (3) there is no consensus characterizing how to discriminate communities from noncommunities, and (4) the application domain includes a wide variety of networks of fundamentally different natures. In this article, we present a class separability framework to tackle these challenges through a comprehensive analysis of community properties. Our approach enables the assessment of the structural dissimilarity among the output of multiple community detection algorithms and between the output of algorithms and communities that arise in practice. In addition, our method provides us with a way to organize the vast collection of community detection algorithms by grouping those that behave similarly. Finally, we identify the most discriminative graph-theoretical properties of community signature and the small subset of properties that account for most of the biases of the different community detection algorithms. We illustrate our approach with an experimental analysis, which reveals nuances of the structure of real and extracted communities. In our experiments, we furnish our framework with the output of 10 different community detection procedures, representative of categories of popular algorithms available in the literature, applied to a diverse collection of large-scale real network datasets whose domains span biology, online shopping, and social systems. We also analyze communities identified by annotations that accompany the data, which reflect exemplar communities in various domain. We characterize these communities using a broad spectrum of community properties to produce the different structural classes. As our experiments show that community structure is not a universal concept, our framework enables an informed choice of the most suitable community detection method for identifying communities of a specific type in a given network and allows for a comparison of existing community detection algorithms while guiding the design of new ones.