Detecting overlapping communities by seed community in weighted complex networks

2013 ◽  
Vol 392 (23) ◽  
pp. 6125-6134 ◽  
Author(s):  
Junqiu Li ◽  
Xingyuan Wang ◽  
Justine Eustace
Keyword(s):  
Complex Networks ◽  
Overlapping Communities ◽  
Weighted Complex Networks

Information mining in weighted complex networks with nonlinear rating projection

2017 ◽  
Vol 51 ◽  
pp. 115-123 ◽  
Author(s):  
Hao Liao ◽  
An Zeng ◽  
Mingyang Zhou ◽  
Rui Mao ◽  
Bing-Hong Wang
Keyword(s):  
Complex Networks ◽  
Information Mining ◽  
Weighted Complex Networks

Finding Overlapping Communities by Increasing the Determinacy of SLPA in Complex Networks

2018 ◽  
pp. 111-122 ◽  
Author(s):  
Jingyi Zhang ◽  
Zhixin Ma ◽  
Qijuan Sun ◽  
Jun Yan ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Overlapping Communities

scholarly journals Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 925
Author(s):  
Shuo Chen ◽  
Zhen Zhang ◽  
Chen Mo ◽  
Qiong Wu ◽  
Peter Kochunov ◽  
...  
Keyword(s):  
Complex Networks ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Graph Embedding ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Healthy Controls ◽  
Weighted Networks ◽  
The Difference ◽  
Weighted Complex Networks

We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analysis techniques in order to address this unmet need. We first perform graph embedding to project all nodes of the weighted adjacency matrix to a low dimensional vector space. Next, we analyze the point distribution pattern in the projected space, and measure its deviation from the complete spatial randomness. We evaluate our method via extensive simulation studies and find that our method can sensitively detect the difference of complexity and is robust to noise. Last, we apply the approach to a functional magnetic resonance imaging study and compare the complexity metrics of functional brain connectivity networks from 124 patients with schizophrenia and 103 healthy controls. The results show that the brain circuitry is more organized in healthy controls than schizophrenic patients for male subjects while the difference is minimal in female subjects. These findings are well aligned with the established sex difference in schizophrenia.

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