Detecting Overlapping Communities in Complex Networks Using Swarm Intelligence for Multi-threaded Label Propagation

2013 ◽  
pp. 111-119 ◽  
Author(s):  
Bradley S. Rees ◽  
Keith B. Gallagher
Keyword(s):  
Complex Networks ◽  
Swarm Intelligence ◽  
Label Propagation ◽  
Overlapping Communities

AGGLOMERATIVE CLUSTERING BASED ON LABEL PROPAGATION FOR DETECTING OVERLAPPING AND HIERARCHICAL COMMUNITIES IN COMPLEX NETWORKS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450021 ◽  
Author(s):  
YUXIN ZHAO ◽  
SHENGHONG LI ◽  
SHILIN WANG
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Large Scale ◽  
Label Propagation ◽  
Agglomerative Clustering ◽  
Overlapping Communities ◽  
Small Communities ◽  
Structural And Functional Properties ◽  
Overlapping Community ◽  
Large Scale Networks

Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in complex networks. By combining the advantages of agglomerative clustering and label propagation, our algorithm can build the hierarchical tree of overlapping communities in large-scale networks. The tests on both synthetic and real-world networks give excellent results and demonstrate the effectiveness and efficiency of our algorithm.

Finding Overlapping Communities by Increasing the Determinacy of SLPA in Complex Networks

2018 ◽  
pp. 111-122 ◽  
Author(s):  
Jingyi Zhang ◽  
Zhixin Ma ◽  
Qijuan Sun ◽  
Jun Yan ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Overlapping Communities

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

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