Detecting Overlapping Communities Based on Community Cores in Complex Networks
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The identification of communities is significant for the understanding of network structures and functions. Since some nodes naturally belong to several communities, the study of overlapping community structures has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. We propose a new algorithm. The main idea is first to find the core of a community by detecting maximal cliques and then merging some tight community cores to form the community. Experimental results on two real networks demonstrate that the present algorithm is more accurate for detecting overlapping community structures, compared with some well-known results and methods.Keywords:
Identification
Clique percolation method
Complex network analysis which can be represented as graph has gained much interest from researchers recently. Analysis derived from complex network leading to a discovery of important group or community lies within the network. It imposes a significant challenge to computer scientists, physicists, and sociologists alike, to identify and discover the true meaning of community for complex network. Different community detection algorithms have been proposed in different perspective of almost similar aim of identifying the community. In this paper, we apply the modularity measurement on complex network and test the strengthness of community found by algorithm proposed. The main study focuses on the importance of having robust algorithm in detecting communities in different type of complex network. Experimental results show that the method is able to successfully separate community by achieving an ideal modularity value.
Modularity
Network Analysis
Clique percolation method
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We propose a method to find the community structure in complex networks based on an extremal optimization of the value of modularity. The method outperforms the optimal modularity found by the existing algorithms in the literature giving a better understanding of the community structure. We present the results of the algorithm for computer-simulated and real networks and compare them with other approaches. The efficiency and accuracy of the method make it feasible to be used for the accurate identification of community structure in large complex networks.
Modularity
Clique percolation method
Complex system
Identification
Value (mathematics)
Extremal optimization
Optimization algorithm
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Comparative Study Of Complex Network Community Structure Algorithms In network Pharmacology Analysis
Community structure is an extremely important characteristic of complex networks composed of network pharmacology. The mining of network community structure is of great importance in many fields such as biology, computer science and sociology. In recent years, for different types of large-scale complex networks, researchers had proposed many algorithms for finding community structures. This paper reviewed some of the most representative algorithms in the field of network pharmacology, and focused on the analysis of the improved algorithms based on the modularity index and the new algorithms that could reflect the level and overlap of the community. Finally, a benchmark was established to measure the quality of the community classification algorithm.
Modularity
Benchmark (surveying)
Network Analysis
Clique percolation method
Network Structure
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Clique percolation method
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Clique percolation method
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Hierarchical network model
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There are many community organizations in social and biological networks. How to identify these community structure in complex networks has become a hot issue. In this paper, an algorithm to detect community structure of networks is proposed by using spectra of distance modularity matrix. The proposed algorithm focuses on the distance of vertices within communities, rather than the most weakly connected vertex pairs or number of edges between communities. The experimental results show that our method achieves better effectiveness to identify community structure for a variety of real-world networks and computer generated networks with a little more time-consumption.
Modularity
Distance matrix
Clique percolation method
Matrix (chemical analysis)
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Modularity
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Due to the development and popularization of Internet, there is more and more research focusing on complex networks. Research shows that there exists community structure in complex networks. Finding out community structure helps to extract useful information in complex networks, so the research on community detection is becoming a hotspot in recent years. There are two remarkable problems in detecting communities. Firstly, the detection accuracy is normally not very high; Secondly, the assessment criteria are not very effective when real communities are unknown. This paper proposes an algorithm for community detection based on hierarchical clustering (CDHC Algorithm). CDHC Algorithm firstly creates initial communities from global central nodes, then expands the initial communities layer by layer according to the link strength between nodes and communities, and at last merges some very small communities into large communities. This paper also proposes the concept of extensive modularity, overcoming some weakness of modularity. The extensive modularity can better evaluate the effectiveness of algorithms for community detection. This paper verifies the advantage of extensive modularity through experiments and compares CDHC Algorithm and some other representative algorithms for community detection on some frequently used datasets, so as to verify the effectiveness and advantages of CDHC Algorithm.
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Clique percolation method
Hierarchical clustering
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Clique percolation method
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Interdependent networks
Clique percolation method
Evolving networks
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