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Dynamic Community Detection

3260 papers • 126 benchmarks • 313 datasets

community detection in dynamic networks

(Image credit: Papersgraph)

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Most implemented papers

IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity

Xing Su, Jianjun Cheng, Haijuan Yang, M. Leng, Wenbo Zhang, Xiaoyun Chen•Tue Jun 30 2020

The results show that the proposed incremental detection method can detect satisfactory community structure from each of snapshot graphs efficiently and steadily, and outperforms the competitors significantly.

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Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection

J. Zou, Fan Lin, Siyu Gao, Gaoshan Deng, Wenhua Zeng, G. Alterovitz•Wed Sep 29 2021

A Feature Transfer Based Multi-Objective Optimization Genetic Algorithm (TMOGA) based on transfer learning and traditional multi-objective evolutionary algorithm framework to extract stable features from past community structures, retain valuable feature information, and integrate this feature information into current optimization processes to improve the evolutionary algorithms.

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Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis

Dexu Kong, Anping Zhang, Yang Li•Fri Jan 05 2024

This paper proposes a novel deep graph clustering framework with temporal consistency regularization on inter-community structures, inspired by the concept of minimal network topological changes within short intervals, and introduces MFC, a matrix factorization-based deep graph clustering algorithm that preserves node embedding.

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