Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Bao, Zhifeng"'
We study structural clustering on graphs in dynamic scenarios, where the graphs can be updated by arbitrary insertions or deletions of edges/vertices. The goal is to efficiently compute structural clustering results for any clustering parameters $\ep
Externí odkaz:
http://arxiv.org/abs/2411.13817
Line charts are a valuable tool for data analysis and exploration, distilling essential insights from a dataset. However, access to the underlying dataset behind a line chart is rarely readily available. In this paper, we explore a novel dataset disc
Externí odkaz:
http://arxiv.org/abs/2408.09506
Traffic accidents pose a significant risk to human health and property safety. Therefore, to prevent traffic accidents, predicting their risks has garnered growing interest. We argue that a desired prediction solution should demonstrate resilience to
Externí odkaz:
http://arxiv.org/abs/2407.19668
Efficient news exploration is crucial in real-world applications, particularly within the financial sector, where numerous control and risk assessment tasks rely on the analysis of public news reports. The current processes in this domain predominant
Externí odkaz:
http://arxiv.org/abs/2405.04929
In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to enhance the integration of temporal dynamics to boost the performance o
Externí odkaz:
http://arxiv.org/abs/2403.11495
Autor:
Wang, Tingting, Huang, Shixun, Bao, Zhifeng, Culpepper, J. Shane, Dedeoglu, Volkan, Arablouei, Reza
In this paper, given a user's query set and a budget limit, we aim to help the user assemble a set of datasets that can enrich a base dataset by introducing the maximum number of distinct tuples (i.e., maximizing distinctiveness). We prove this probl
Externí odkaz:
http://arxiv.org/abs/2401.00659
Autor:
Peng, Tianhao, Wu, Wenjun, Yuan, Haitao, Bao, Zhifeng, Pengrui, Zhao, Yu, Xin, Lin, Xuetao, Liang, Yu, Pu, Yanjun
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and dif
Externí odkaz:
http://arxiv.org/abs/2312.09708
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years. In this article, we conduct an in-depth and comprehensive review,
Externí odkaz:
http://arxiv.org/abs/2309.10979
While in-memory learned indexes have shown promising performance as compared to B+-tree, most widely used databases in real applications still rely on disk-based operations. Based on our experiments, we observe that directly applying the existing lea
Externí odkaz:
http://arxiv.org/abs/2306.02604