Abstract
Structural graph clustering (\(\textsf{SCAN}\)) is a foundational problem about managing and profiling graph datasets, which is widely experienced across many realistic scenarios. Due to existing work on structural graph clustering focused on unsigned graphs, existing \(\textsf{SCAN}\) methods are not applicable to signed networks that can indicate friendly and antagonistic relationships. To tackle this problem, we investigate a novel structural graph clustering model, named \(\textsf{SSCAN}\). On the basis of \(\textsf{SSCAN}\), we propose an online approach that can efficiently compute the clusters for a given signed network. Furthermore, we also devise an efficient index structure, called \(\mathsf {SSCAN\text {-}Index^{+}}\), which stores information about core vertices and structural similarities. The size of our proposed index can be well bounded by O(m), where m is the total amount of edges in an input signed network. Following the new index \(\mathsf {SSCAN\text {-}Index^{+}}\), we develop an index-based query method designed to avoid invalid scans of the entire network. Extensive experimental testings on eight real signed networks prove the effectiveness and efficiency of our proposed methods.
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Acknowledgements
This research was sponsored by the Fundamental Research Funds for the Central Universities, 3072022TS0605, China University Industry University-Research Innovation Fund, 2021LDA10004, and National Natural Science Foundation of China, 62272126.
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Zhao, Z., Li, W., Meng, X., Wang, X., Lv, H. (2024). SSCAN:Structural Graph Clustering on Signed Networks. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_26
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DOI: https://doi.org/10.1007/978-981-97-2390-4_26
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