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Finding Core-Periphery Structures With Node Influences
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AI
DOI：10.1109/TNSE.2021.3138436.png)
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摘要
En 中文
Detecting core-periphery structures is one of the outstanding issues in complex network analysis. Various algorithms can identify core nodes and periphery nodes. Recent advances found that many networks from real-world data can be better modeled with multiple pairs of core-periphery nodes. In this study, we propose to use an influence propagation process to detect multiple pairs of core-periphery nodes. In this framework, we assume each node can emit a certain amount of influence and propagate it through the network. Then we identify nodes with large influences as core nodes, and we utilize a maximum influence chain to construct a node-pairing network to determine core-periphery pairs. This approach can take node interactions into consideration and can reduce noises in finding pairs. Experiments on randomly generated networks and real-world networks confirm the efficiency and accuracy of our algorithm.
Keyword:
Detection algorithms
Complex networks
Image edge detection
Periodic structures
Stochastic processes
Psychology
Proteins
Core-periphery structure
complex networks
node influences
computational modeling
unsupervised learning
期刊
I
IF:
7.9
论文数:
2.5K
被引数:
10.0K
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引用论文
Overlapping Communities Explain Core-Periphery Organization of Networks.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
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