High-quality community detection for large-scale complex networks usually depends on the topology of the graph, which used to divide the node set. However, real-world networks usually have noisy and cluster-independent links, which may cause the model to divide the nodes from different clusters together. This paper proposed a algorithm called graph reconstruction based community detection(GRCD). This method could handle community detection in large-scale complex networks. GRCD firstly deleted the connected edges between communities to reconstruct the community structure of the original graph. Subsequently, GRCD regarded the network as a social system, aiming to reveal the community in a more intuitive way. This paper proposed an efficient community detection strategy: a community organization generation strategy based on discourse power. Extensive experiments were performed on datasets of different sizes. The results show that GRCD can not only deal with large-scale networks, but also has strong competitiveness in the quality of its community division compared with several existing benchmark algorithms while maintaining high stability.

[2022-09-19] Accepted Paper /doc/2022.07.0331/v1

[2023-02-05] Printed Article /doc/2022.07.0331/v2