HURST is a Heterogeneity-Adaptive URban Foundation Model for Spatio-Temporal Prediction that is capable of capturing the spatial pattern of heterogeneity underlying the urban setting to enhance the UFM's performance and presents two key technical innovations.
Zirui Zhou, Xun Zhou, Kanyu Bao et al.· Proceedings of the 32nd ACM...· 0 citations
Cohesive subgraph mining is a fundamental task in graph data analytics. We re-visit the problem of listing all minimal $k$-cores, where a $k$-core is a subgraph in which every vertex has degree at least $k$, and minimality requires that no proper subset remains a $k$-core. Existing methods are computationally prohibiti...
Yukai Sun, Kaiqiang Yu, Shengxin Liu et al.· IEEE International Conferenc...· 0 citations
Cohesive subgraph mining has been extensively studied and finds numerous graph mining applications such as link farm identification, community detection, and product recommendation. Among various cohesive subgraph structures, the <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k...
Qifan Zhang, Yang Liu, Kaiqiang Yu et al.· IEEE Transactions on Knowled...· 0 citations
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