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20052023
most citedThe clustering coefficient and community structure of bipartite networks

209 citations · 495 across the 9 of their papers we have counts for

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physics.soc-ph2023

Community detection based on structural balance in signed networks

Peng Zhang, Xianyu Xu, Leyang Xue

In signed networks, some existing community detection methods treat negative connections as intercommunity links and positive ones as intracommunity links. However, it is important…

physics.soc-ph2021★ 1 cited

Maximizing spreading in complex networks with risk in node activation

Leyang Xue, Peng Zhang, An Zeng

It is widely acknowledged that the initial spreaders play an important role for the wide spreading of information in complex networks. Thus, a variety of centrality-based methods h…

physics.soc-ph2008★ 5 cited

Empirical Analysis and Evolving Model of Bipartite Networks

Peng Zhang, Menghui Li, J. F. F. Mendes +2

Many real-world networks display a natural bipartite structure. Investigating it based on the original structure is helpful to get deep understanding about the networks. In this pa…

physics.soc-ph2008★ 89 cited

A New Comparative Definition of Community and Corresponding Identifying Algorithm

Yanqing Hu, Hongbin Chen, Peng Zhang +3

In this paper, a new comparative definition for community in networks is proposed and the corresponding detecting algorithm is given. A community is defined as a set of nodes, whic…

physics.soc-ph2007★ 89 cited

Community Detecting By Signaling on Complex Networks

Yanqing Hu, Menghui Li, Peng Zhang +2

Based on signaling process on complex networks, a method for identification community structure is proposed. For a network with nodes, every node is assumed to be a system whic…

physics.soc-ph2007★ 209 cited

The clustering coefficient and community structure of bipartite networks

Peng Zhang, Jinliang Wang, Xiaojia Li +2

Many real-world networks display a natural bipartite structure. It is necessary and important to study the bipartite networks by using the bipartite structure of the data. Here we…