Abstract
Geographical entity community discovery aims at discovering geographical entities that are closely related to each other. It has shown great practical value in many social applications such as economic development, administrative management, and resource utilization. Traditional research methods use network topology to represent the relationship between geographical entities, but cannot accurately define semantic relationships. To deal with this problem, this paper uses the network news that provides massive semantic information, and integrates the semantic information and spatial information into the geographic entity community discovery algorithm. An edge weight calculation method based on semantic association strength, geographic entity influence, and boundary connection distance is proposed. Experimental analyses show that compared with existing methods, the new algorithm improves the accuracy of spatial community division. A case study on real-world datasets shows that the experimental results of the new method are better than the existing methods and are more satisfied with common sense.
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References
Goodchild, M.F.: Geographic information systems. Prog. Hum. Geogr. 15(2), 194–200 (1991)
Daosheng, D.: The science of cartography in China. Cartogr. J. 21(2), 145–147 (1984)
Larson, R.R.: Ranking approaches for GIR. Sigspatial Spec. 3(2), 37–41 (2011)
Ni, L., Luo, W., Zhu, W., Hua, B.: Local overlapping community detection. ACM Trans. Knowl. Discov. Data (TKDD) 14(1), 1–25 (2019)
Zhang, R., Jin, Z., Xu, P., Liu, X.: A dynamic clustering based on community detection. Cluster Comput. 22(3), 5703–5711 (2019)
Qian, K.: Research on hierarchical clustering community discovery algorithm based on diverging number. Shanxi University (2015)
Meknassi, M., Aboulhamid, E.M., Cerny, E.: Algorithm for the graph-partitioning problem using a problem transformation method. Comput. Aided Des. 24(7), 397–398 (1992)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., et al.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)
Purves, R.S., et al.: The design and implementation of SPIRIT: a spatially aware search engine for information retrieval on the Internet. Int. J. Geogr. Inf. Sci. 21(7) 717–745 (2007)
Stewart, J.Q.: Demographic gravitation: evidence and applications. Sociology 11(1/2) 31–58 (1948)
Yan, M., Jing, N., Zhong, Z., et al.: Geographical entity community mining based on spatial and semantic association. In: Proceedings of the 3rd International Conference on Computer Science and Application Engineering, pp. 1–6 (2019)
Socher, R., Bengio, Y., Manning, C.D.: Deep Learning for NLP (without magic) Tutorial Abstracts of ACL 2012, p. 5 (2012)
Shrestha, A., Mahmood, A.: Review of deep learning algorithms and architectures. IEEE Access 7, 53040–53065 (2019)
Katz, E., Lazarsfeld, P.F.: Personal Influence, The Part Played by People in the Flow of Mass Communications, pp. 1–12. Free Press, New York (1955)
Verikas, A., Gelzinis, A., Bacauskiene, M.: Mining data with random forests: a survey and results of new tests. Pattern Recogn. 44(2), 330–349 (2011)
Wind, Y., Saaty, T.L.: Marketing applications of the analytic hierarchy process. Manage. Sci. 26(7), 641–658 (1980)
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Yu, M., Wang, Z., Pang, Y., Xu, Y. (2021). Geographical Entity Community Discovery Based on Semantic Similarity. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_35
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DOI: https://doi.org/10.1007/978-3-030-84529-2_35
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