Multi-objective community detection algorithm with node importance analysis in attributed networks

Detecting community structures helps to reveal the functional units of complex networks. In this paper, the community detection problem is regarded as a modularity-based multi-objective optimization problem (MOP), and a parallel conical area community detection algorithm (PCACD) is designed to solve this MOP effectively and efficiently. In consideration of the global properties of the selection and update mechanisms, PCACD employs a global island model and targeted elitist migration policy in a conical area evolutionary algorithm (CAEA) to discover community structures at different resolutions in parallel. Although each island is assigned only a portion of all sub-problems in the island model, it preserves a complete population to accomplish the global selection and update. Meanwhile the migration policy directly migrates each elitist individual to an appropriate island in charge of the sub-problem associated with this individual to share essential evolutionary achievements. In addition, a modularity-based greedy local search strategy is also applied to accelerate the convergence rate. Comparative experimental results on six real-world networks reveal that PCACD is capable of discovering potential high-quality community structures at diverse resolutions with satisfactory running efficiencies.

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Overlapping Community Detection Based on Node Importance and Adjacency Information

Security and Communication Networks ◽

10.1155/2021/8690662 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Ping Wang ◽

Yonghong Huang ◽

Fei Tang ◽

Hongtao Liu ◽

Yangyang Lu

Keyword(s):

Community Structure ◽

Social Network ◽

Community Detection ◽

Evaluation Method ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Node Importance ◽

Overlapping Community ◽

Community Detection Algorithm

Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

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A novel node gravitation-based label propagation algorithm for community detection

International Journal of Modern Physics C ◽

10.1142/s0129183119500499 ◽

2019 ◽

Vol 30 (06) ◽

pp. 1950049 ◽

Cited By ~ 6

Author(s):

Mengjia Shen ◽

Zhixin Ma

Keyword(s):

Community Detection ◽

Prior Information ◽

Linear Time ◽

Detection Algorithm ◽

Label Propagation ◽

Node Importance ◽

Propagation Algorithm ◽

Community Detection Algorithm ◽

And Function

Community detection in networks is a very important area of research for revealing the structure and function of networks. Label propagation algorithm (LPA) has been widely used to detect communities in networks because it has the advantages of linear time complexity and is unnecessary to get prior information, such as objective function and the number of communities. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the algorithm. In this paper, we propose a novel community detection algorithm, named NGLPA, in which labels are propagated by node gravitation defined by node importance and similarity between nodes. To select the label according to the gravitation between nodes can reduce the randomness of LPA and is consistent with reality. The proposed method is tested on several synthetic and real-world networks with comparative algorithms. The results show that NGLPA can significantly improve the quality of community detection and obtain accurate community structure.

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