Abstract
Living circle structure reflects the relationship between community residents and surrounding living facilities. Through the study of urban community structure, we can understand the distribution of facilities in the living circle. Most of the existing related work studies the structure of urban communities from the perspective of macro and large-scale, lacking of research on fine-grained and small-scale community. This paper defines the structure of the multi-layer living circle and uses the representation learning method to obtain the structural characteristics of the living circle through the activities of the residents in the surrounding POIs. First, a representation framework of multi-layer living circle structure is proposed. Second, the autoencoder representation learning is used to construct the dynamic activity graphs of the multi-layer living circle and the vector representation of the potential characteristics of the living circle effectively summarizes the multi-layer living circle structure. Finally, an experimental evaluation of the proposed multi-layer living circle structure uses real datasets to verify the validity of the proposed methods in terms of community convenience and community similarity applications.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (62073227), the National key R & D Program Foundation, Natural Science Foundation of Liaoning Province (2019-MS-264), the Project of the Educational Department of Liaoning Province (LJZ2021008), Project of China Academic Degree and Graduate Education Association(2020MSA40).
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Wang, H., Liu, J., Peng, C., Sun, H. (2023). Representation Learning of Multi-layer Living Circle Structure. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_11
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DOI: https://doi.org/10.1007/978-981-99-6222-8_11
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