Abstract
The representation learning approach aims to obtain a low-dimensional representation of nodes and accomplish community detection by clustering. Adjacency matrix is the most common form of network representation, but it only represents the direct connection relationship of network nodes and lacks more useful topological information. Existing approaches, such as jaccard coefficient for topology extraction, are still limited to neighborhoods, and the available information is not rich enough. In addition, roles, another vital idea, lack a more profound application to network topology. This paper proposes a novel community detection algorithm based on enhancing graph autoencoder with node structural role (CDESR). On the one hand, the structural role we designed effectively specifies the importance of nodes in the network. Based on this idea, a new strategy for computing node topological relations is proposed for their information extraction. On the other hand, the enhancement matrix constructed using the extracted rich information efficiently optimizes the graph autoencoder to obtain a high-quality representation. The experimental results on real-world and synthetic networks verify the effectiveness of our algorithm.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 62002063, in part by the Fujian Natural Science Funds under Grant 2020J05112, in part by the Funds of Fujian Provincial Department of Education under Grant JAT190026, and in part by the Fuzhou University under Grant 510872/GXRC-20016, the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, in part by the National Key Research and Development Plan of China under Grant No. 2021YFB3600503, in part by the Fujian Collaborative Innovation Center for Big Data Applications in Governments, in part by the Fujian Industry-Academy Cooperation Project under Grant No. 2018H6010, in part by the Natural Science Foundation of Fujian Province under Grant No. 2020J05112, in part by the Fujian Provincial Department of Education under Grant No. JAT190026, in part by the Major Science and Technology Project of Fujian Province under Grant No.2021HZ022007 and Haixi Government Big Data Application Cooperative Innovation Center and the China Scholarship Council under Grant 202006655008.
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Wu, L., Yang, J., Guo, K. (2023). Community Detection Based on Enhancing Graph Autoencoder with Node Structural Role. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_18
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DOI: https://doi.org/10.1007/978-981-99-2356-4_18
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