Zobrazeno 1 - 10
of 1 092
pro vyhledávání: '"Zhou, Xiaofang"'
Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing is an essential goal for the real-time response of those services. However, the large number of queries submitted
Externí odkaz:
http://arxiv.org/abs/2409.06148
The routing results are playing an increasingly important role in transportation efficiency, but they could generate traffic congestion unintentionally. This is because the traffic condition and routing system are disconnected components in the curre
Externí odkaz:
http://arxiv.org/abs/2409.11412
Autor:
Wang, Jialiang, Di, Shimin, Liu, Hanmo, Wang, Zhili, Wang, Jiachuan, Chen, Lei, Zhou, Xiaofang
Graph Neural Networks (GNNs), like other neural networks, have shown remarkable success but are hampered by the complexity of their architecture designs, which heavily depend on specific data and tasks. Traditionally, designing proper architectures i
Externí odkaz:
http://arxiv.org/abs/2408.06717
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing. VFL facilitates collaboration among multiple entities with distinct feature
Externí odkaz:
http://arxiv.org/abs/2405.02364
Autor:
Li, Shuhao, Cui, Yue, Xu, Jingyi, Li, Libin, Meng, Lingkai, Yang, Weidong, Zhang, Fan, Zhou, Xiaofang
Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years. With the advancement of Vehicle-to-Everything (V2X) technologies, autonomous driving
Externí odkaz:
http://arxiv.org/abs/2403.14941
Autor:
Chen, Wei, Liang, Yuxuan, Zhu, Yuanshao, Chang, Yanchuan, Luo, Kang, Wen, Haomin, Li, Lei, Yu, Yanwei, Wen, Qingsong, Chen, Chao, Zheng, Kai, Gao, Yunjun, Zhou, Xiaofang, Zheng, Yu
Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical applications such as location services, urban traffic, and public safety. Traditi
Externí odkaz:
http://arxiv.org/abs/2403.14151
Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environmen
Externí odkaz:
http://arxiv.org/abs/2402.15247
Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually com
Externí odkaz:
http://arxiv.org/abs/2402.05154
Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. However, the proper valuation of shared data in this collaborative process remains insufficiently addressed. In this work, we frame
Externí odkaz:
http://arxiv.org/abs/2402.01802
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges. The core concept of HGFL is to extract knowledge from rich-labeled c
Externí odkaz:
http://arxiv.org/abs/2401.03597