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20132025
most citedMarkov random walk under constraint for discovering overlapping communities in complex networks

51 citations · 185 across the 18 of their papers we have counts for

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8 papers · 1 filter

cs.SI2020★ 2 cited

GCN for HIN via Implicit Utilization of Attention and Meta-paths

Di Jin, Zhizhi Yu, Dongxiao He +3

Heterogeneous information network (HIN) embedding, aiming to map the structure and semantic information in a HIN to distributed representations, has drawn considerable research att…

cs.SI2013

Identification of hybrid node and link communities in complex networks

Dongxiao He, Di Jin, Weixiong Zhang

Identification of communities in complex networks has become an effective means to analysis of complex systems. It has broad applications in diverse areas such as social science, e…

cs.SI2013★ 19 cited

Ant Colony Optimization with a New Random Walk Model for Community Detection in Complex Networks

Di Jin, Dayou Liu, Bo Yang +2

Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walk…

cs.SI2013★ 29 cited

Genetic Algorithm with a Local Search Strategy for Discovering Communities in Complex Networks

Dayou Liu, Di Jin, Carlos Baquero +3

In order to further improve the performance of current genetic algorithms aiming at discovering communities, a local search based genetic algorithm GALS is here proposed. The core…

cs.SI2013★ 51 cited

Markov random walk under constraint for discovering overlapping communities in complex networks

Di Jin, Bo Yang, Carlos Baquero +3

Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, ca…

cs.SI2013★ 29 cited

Genetic Algorithm with Ensemble Learning for Detecting Community Structure in Complex Networks

Dongxiao He, Zhe Wang, Bin Yang +1

Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly a…