Abstract
Clustering is a basic technology in data mining, and similarity measurement plays a crucial role in it. The existing clustering algorithms, especially those for social networks, pay more attention to users’ properties while ignoring the global measurement across social relationships. In this paper, a new clustering algorithm is proposed, which not only considers the distance of users’ properties but also considers users’ social influence. Social influence can be further divided into mutual influence and self influence. With mutual influence, we can deal with users’ interests and measure their similarities by introducing areas and activities, thus better weighing the influence between them in an indirect way. Separately, we formulate a new propagation model, PR-Threshold++, by merging the PageRank algorithm and Linear Threshold model, to model the self influence. Based on that, we design a novel similarity by exploiting users’ distance, mutual influence, and self influence. Finally, we adjust K-medoids according to our similarity and use real-world datasets to evaluate their performance in intensive simulations.
This work was supported in part by the National Key R&D Program of China [2020YFB1707900], the National Natural Science Foundation of China (NSFC) [62202055, 62272302, 62172276], and Shanghai Municipal Science and Technology Major Project [2021SHZDZX0102].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edition. Morgan Kaufmann (2011)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), pp. 137–146 (2003)
Kim, W., Kanezaki, A., Tanaka, M.: Unsupervised learning of image segmentation based on differentiable feature clustering. IEEE Trans. Image Process. 29, 8055–8068 (2020)
Knattrup, Y., Kubecka, J., Ayoubi, D., Elm, J.: Clusterome: a comprehensive data set of atmospheric molecular clusters for machine learning applications. ACS Omega 8(28), 25155–25164 (2023)
Li, Y., Gao, H., Gao, Y., Guo, J., Wu, W.: A survey on influence maximization: from an ml-based combinatorial optimization. ACM Trans. Knowl. Discov. Data 17(9), 133:1–133:50 (2023)
Mishra, P.K., Verma, S.K.: A survey on clustering in wireless sensor network. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–5. IEEE (2020)
Park, H.S., Jun, C.H.: A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36(2, Part 2), 3336–3341 (2009)
Parker, A.J., Barnard, A.S.: Selecting appropriate clustering methods for materials science applications of machine learning. Adv. Theory Simul. 2(12), 1900145 (2019)
Ran, X., Zhou, X., Lei, M., Tepsan, W., Deng, W.: A novel k-means clustering algorithm with a noise algorithm for capturing urban hotspots. Appl. Sci. 11(23), 11202 (2021)
Rehioui, H., Idrissi, A., Abourezq, M., Zegrari, F.: DENCLUE-IM: a new approach for big data clustering. Int. Conf. Ambient Syst. Netw. Technol. (ANT) 83, 560–567 (2016)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–93 (2008)
Taunk, K., De, S., Verma, S., Swetapadma, A.: A brief review of nearest neighbor algorithm for learning and classification. In: 2019 International Conference on Intelligent Computing and Control Systems (ICCS), pp. 1255–1260. IEEE (2019)
Velmurugan, T., Santhanam, T.: Computational complexity between k-means and k-medoids clustering algorithms for normal and uniform distributions of data points. J. Comput. Sci. 6(3), 363–368 (2010)
Zhang, H., Li, H., Chen, N., Chen, S., Liu, J.: Novel fuzzy clustering algorithm with variable multi-pixel fitting spatial information for image segmentation. Pattern Recogn. 121, 108201 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, J., Zhu, Z., Gao, Y., Gao, X. (2024). Graph Clustering Through Users’ Properties and Social Influence. In: Wu, W., Guo, J. (eds) Combinatorial Optimization and Applications. COCOA 2023. Lecture Notes in Computer Science, vol 14462. Springer, Cham. https://doi.org/10.1007/978-3-031-49614-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-49614-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49613-4
Online ISBN: 978-3-031-49614-1
eBook Packages: Computer ScienceComputer Science (R0)