Community detection algorithm with locally social spider optimized

Li, Youhong; Liu, Ruobing; Qiu, Zhihong

doi:10.1109/itoec.2017.8122542

Cited by 1 publication

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods follow the idea of starting from a few nodes or clusters in the network and gradually expanding the communities to the neighborhood until reaching the maximum value of the community evaluation indexes. In recent years, by introducing heuristic methods, the efficiency and stability of local search in local community detection are enhanced, such as using the swarm intelligence optimization framework in LSSO/CD [13]. The local methods can greatly improve the efficiency of community detection through parallel computing, but they inevitably involve three issues: first, how to define a good local community evaluation index; second, how to make full use of global information; and third, how to handle the edge nodes, overlapping community nodes, and orphan nodes.…”

Section: Related Workmentioning

confidence: 99%

A Multi-label Propagation Algorithm for Community Detection Based on Average Mutual Information

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Community structure is one of the vital characteristics of complex networks. How to effectively detect communities is a hot issue. From the perspective of information theory, the community structure of complex networks can be detected and revealed more accurately. This research introduces the average mutual information (AMI) into the detection process of the multi-label propagation algorithm (MLPA) and proposes a new community detection algorithm AMI-MLPA. The algorithm initially determines the propagation order according to the influence of nodes in the network. By selecting the label with stronger propagation intensity and smaller conditional entropy in the process of label propagation, a more reasonable community partition can be obtained. Experiments on real-world datasets and synthetic datasets show that the algorithm is better than FastGN, GN, and other LPA-based algorithms in general, with high accuracy of results on large-scale synthetic networks, which verifies the effectiveness of the algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

A Multi-label Propagation Algorithm for Community Detection Based on Average Mutual Information

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Community detection algorithm with locally social spider optimized

Cited by 1 publication

References 13 publications

A Multi-label Propagation Algorithm for Community Detection Based on Average Mutual Information

A Multi-label Propagation Algorithm for Community Detection Based on Average Mutual Information

Contact Info

Product

Resources

About