An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

Aiming at the problems of existing algorithms, such as instability, neglecting interaction between nodes and neglecting attributes of node, an overlapping community discovery algorithm combining topological potential and trust value of nodes was proposed. Firstly, the importance of nodes is calculated according to topological potential and the trust value of the node, and then K core nodes are selected. In final, the final division of communities are finished by using the extended modularity and core nodes. Experimental results on LFR network datasets and three real network datasets, verify the efficiency of the proposed OCDTT algorithm.

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“…Table 1 illustrates four groups parameter information of simulation LFR network. [3,8]. Range of threshold in LINK is [0.1, 0.9] and step is 0.1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…For example, when pathmin(vi, vj) = 1, vj is 1-order neighbor node of vi. The trust value of the node vi is defined as the sum of the similarity s(vi, vj) between vi and all its p-order neighbors [8]. A i ( , )…”

Section: Trust Value Of Nodesmentioning

confidence: 99%

Overlapping Community Detection Combining Topological Potential and Trust Value of Nodes

Kong

Wei

et al. 2020

IFIP Advances in Information and Communication Technology

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“…where d ij denotes the network distance or hop count between node v i and node v j , influencing factors σ are used to control the influence scope of each node, and m(v i ) denotes the weight of node v i , which is used to describe the inherent attribute of each node. Through similar studies [27]- [30], let m(v i ) = 1 in this paper. According to the mathematical properties of the Gaussian function, if d ij > 3σ/ √ 2 , the topological potential influence of node v i to node v j will rapidly decay to 0 with distance, which can be neglected.…”

Section: Definition 2 (Topological Potential): Given a Networkmentioning

confidence: 99%

The Multi-Dimensional Information Fusion Community Discovery Based on Topological Potential

Fei

et al. 2020

IEEE Access

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Many community discovery algorithms add attribute information of nodes to further improve the quality of community division in the complex network with redundant and discrete data, but these algorithms lack of multi-dimensional information, such as users' interests in social networks, social relations, geography and education background, in addition to topological structure and attribute information. Therefore,this paper proposes a Multi-dimensional Information Fusion Community Discovery(MIFCD) method. Firstly, based on the idea of label propagation, link information and attribute information are combined to get link weights between nodes. Secondly, link weights are added to the topology potential to divide the sub group communities. Finally, the sub group communities are combined by using the distance information and attribute information of the core nodes between communities. In order to verify the effectiveness of the algorithm proposed in this paper, the algorithm is compared with six community partition algorithms which only consider the link information of nodes and consider the two kinds of information of node attributes and links. Experiment results on eight social networks show that this method can effectively improve the quality of community classification in both attribute communities and non-attribute communities by analyzing four evaluation indexes: improved modular degree, information entropy, community overlap degree and comprehensive index. INDEX TERMS Community division, complex network, discrete data, multi-dimensional information.

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“…(1) We propose an edge-reweighting method. More specific, different from the existing approaches [23], [24], we comprehensively consider the network edge information and network topology information to recalculate edges weight, which is to enhance the weight of the community edges further. (2) Different from the existing seed expansion methods [19], [25], we redefine the weight of vertices in the network according to the new edges weight, select a vertex with the highest weight as community seed vertex, and then iterate to obtain weighted dense subgraphs according to the seed expansion strategy and the weight updating method in linear time.…”

Section: Introductionmentioning

confidence: 99%

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

Jiang

Wang

et al. 2019

IEEE Access

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A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy. INDEX TERMS Overlapping community detection, complex network, weighted network, dense subgraph.

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An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

Cited by 6 publications

References 7 publications

Overlapping Community Detection Combining Topological Potential and Trust Value of Nodes

Overlapping Community Detection Combining Topological Potential and Trust Value of Nodes

The Multi-Dimensional Information Fusion Community Discovery Based on Topological Potential

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

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