A spiderweb model for community detection in dynamic networks

Yang, Haibin; Cheng, Jianjun; Su, Xing; Zhang, Wenbo; Zhao, Shiyan; Chen, Xiaoyun

doi:10.1007/s10489-020-02059-7

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…Mishra et al [ 45 ] proposed a tree-based structure in dynamic networks that uses connection and penetration methods to maintain and record information about the discovery and change of communities at different time intervals. The spiderweb method is suggested in [ 46 ], which uses the idea of a spider web by forming subgraphs of nodes to identify a community. In [ 47 ], a new method for identifying dynamic communities is introduced, which works based on the distance between the resistance, identifying the core node, and using a noise community.…”

Section: Related Workmentioning

confidence: 99%

A Multiagent Memetic Optimization Algorithm Based on Temporal Asymptotic Surprise in Complex Networks to Reveal the Structure of the Dynamic Community

Ranjkesh

Masoumi

Hashemi

2022

Computational Intelligence and Neuroscience

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Complex networks are used in a variety of applications. Revealing the structure of a community is one of the essential features of a network, during which remote communities are discovered in a complex network. In the real world, dynamic networks are evolving, and the problem of tracking and detecting communities at different time intervals is raised. We can use dynamic graphs to model these types of networks. This paper proposes a multiagent optimization memetic algorithm in complex networks to detect dynamic communities and calls it DYNMAMA (dynamic multiagent memetic algorithm). The temporal asymptotic surprise is used as an evaluation function of the algorithm. In the proposed algorithm, work is done on dynamic data. This algorithm does not need to specify the number of communities in advance and meets the time smoothing limit, and this applies to dynamic real-world and synthetic networks. The results of the performance of the evaluation function show that this proposed algorithm can find an optimal and more convergent solution compared to modern approaches.

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Section: Related Workmentioning

confidence: 99%