Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Wu, Ruofan"'
Autor:
Liu, Yunfei, Li, Jintang, Chen, Yuehe, Wu, Ruofan, Wang, Ericbk, Zhou, Jing, Tian, Sheng, Shen, Shuheng, Fu, Xing, Meng, Changhua, Wang, Weiqiang, Chen, Liang
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering a
Externí odkaz:
http://arxiv.org/abs/2406.14288
Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors. In practice, temporal graphs are formalized as an ordered sequence of
Externí odkaz:
http://arxiv.org/abs/2406.00943
Policy gradient methods in actor-critic reinforcement learning (RL) have become perhaps the most promising approaches to solving continuous optimal control problems. However, the trial-and-error nature of RL and the inherent randomness associated wit
Externí odkaz:
http://arxiv.org/abs/2404.11834
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vu
Externí odkaz:
http://arxiv.org/abs/2402.04033
Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
We address the issue of estimation bias in deep reinforcement learning (DRL) by introducing solution mechanisms that include a new, twin TD-regularized actor-critic (TDR) method. It aims at reducing both over and under-estimation errors. With TDR and
Externí odkaz:
http://arxiv.org/abs/2311.03711
Autor:
Wu, Ruofan, Zhang, Mingyang, Lyu, Lingjuan, Xu, Xiaolong, Hao, Xiuquan, Fu, Xinyi, Liu, Tengfei, Zhang, Tianyi, Wang, Weiqiang
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management
Externí odkaz:
http://arxiv.org/abs/2310.20552
Autor:
Li, Jintang, Wei, Zheng, Dan, Jiawang, Zhou, Jing, Zhu, Yuchang, Wu, Ruofan, Wang, Baokun, Zhen, Zhang, Meng, Changhua, Jin, Hong, Zheng, Zibin, Chen, Liang
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs
Externí odkaz:
http://arxiv.org/abs/2310.11664
Autor:
Dan, Jiawang, Wu, Ruofan, Liu, Yunpeng, Wang, Baokun, Meng, Changhua, Liu, Tengfei, Zhang, Tianyi, Wang, Ningtao, Fu, Xing, Li, Qi, Wang, Weiqiang
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the most prevailing algorithmic tool. Despite its popularity, the family
Externí odkaz:
http://arxiv.org/abs/2310.11281
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN
Externí odkaz:
http://arxiv.org/abs/2309.09517
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contrastive learning (GCL) is emerging as a promising way of leveraging vast
Externí odkaz:
http://arxiv.org/abs/2306.02117