Abstract
The prosperity of smart mobile devices and the popularity of social networks have led to the rapid growth of spatial social networks. Spatial-aware community search aims to look for a cohesive subgraph that contains a query vertex in spatial social networks, whose vertices are close structurally and spatially. However, existing studies only focus on homogeneous graphs, and ignore the heterogeneity of the networks, which results in the searched community is not refined enough to meet the specific applications of scenarios. In this paper, we propose a novel problem, named spatial-aware community search over a heterogeneous information network (SACS-HIN), which retrieves a refined community by capturing rich semantics in the network, taking into account spatial proximity and social relevance. To solve this problem, we develop three algorithms based on the structure-first strategy and distance-first strategy. Finally, extensive experiments are conducted on four datasets to evaluate both the effectiveness and efficiency of our proposed algorithms. The community size analysis and case study verify that the proposed algorithms can obtain a refined community that satisfies query conditions. The efficiency evaluation explores the effect of different parameters on the efficiency of the algorithms.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (62062066, 61762090, 61966036), Yunnan Fundamental Research Projects (202201AS070015), the Scientific Research Fund Project of Yunnan Provincial Education Department (2023Y0249), and the Postgraduate Research and Innovation Foundation of Yunnan University (2021Y024).
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Zhou, Y., Zhou, L., Wang, J., Wang, L., Kong, B. (2023). Spatial-Aware Community Search Over Heterogeneous Information Networks. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_7
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