Comparison of four variants of RE-GCN.

<div><p>Community detection is a classical problem for analyzing the structures of various graph-structured data. An efficient approach is to expand the community structure from a few structure centers based on the graph topology. Considering them as pseudo-labeled nodes, graph convoluti...

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Bibliographic Details
Main Author:	Bing Guo (25387) (author)
Other Authors:	Liping Deng (756374) (author), Tao Lian (10662498) (author)
Published:	2025
Subjects:	Biochemistry Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified refinement process yields extensive experiments demonstrate corresponding induced subgraph structure centers based representative structure centers refined structure centers initial structure centers inappropriate structure centers highest structure importance current structure centers label propagation ability temporary graph partition structure center refinement realize community detection community detection performance graph convolutional network labeled set expansion gcn ), considering structure center community structure network topology label information various graph labeled set labeled nodes graph topology graph convolution entire graph two steps structured data shallow gcn recently exploited proposed approach localized filter limited amount iteratively refine highly dependent gcn improves final gcn efficient approach classical problem adverse effect
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Comparison of four variants of RE-GCN.

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