Abstract
Community detection is an important method for network organizations exploration. This method has been widely employed in application systems and proves beneficial. As a complex network, the domain knowledge graph often has a small number of known community structures, and the use of these community structure information can effectively improve the effect of community detection. Based on this community structure and the self-similar characteristics of complex networks, this paper proposes a supervised learning community detection method, the core of which is the Attachment Graph Model (AGM). This model effectively utilizes the known community structure information, calculates the attachment strength between nodes based on supervised learning algorithms, determines the attachment relationship of the nodes to form an attachment matrix, thereby able to perform community testing to the entire domain knowledge graph. The community detection method (AGM) proposed in this paper is compared with the previous community detection methods in the real enterprise investment relationship network. The results show that AGM demonstrates a higher community detection accuracy.
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This work was supported in part by the National Key Research and Development Program of China under Grants 2020YFC1807104.
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Zhao, Y., Yan, H., Zhao, X. (2022). A Supervised Learning Community Detection Method Based on Attachment Graph Model. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_22
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