1 citations · 1 across the 6 of their papers we have counts for
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CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning
PinHeng Zong, Ye Yuan
Dynamic graphs are widely used to model time-evolving relational systems in real-world applications. Dynamic graph neural networks provide an effective framework for capturing both…
A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis
Ye Yuan, Song Wang, Hongxun Zhou +2
Predicting temporal Quality of Service (QoS) data is critical for optimizing network services and rationalizing resource allocation in cloud computing and service-oriented systems.…
Heterogeneous Graph Condensation via Role-Aware Clustering
Fuyan Ou, Yulin Hu, Ye Yuan
Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-s…
An Efficient and Scalable Graph Condensation with Structure-Preserving
Yulin Hu, Fuyan Ou, Ye Yuan
Graph condensation (GC) is pivotal for enabling Graph Neural Networks (GNNs) deployment in resource-constrained scenarios by compressing large-scale graphs into compact synthetic c…
Neighbor Overlay-Induced Graph Attention Network
Tiqiao Wei, Ye Yuan
Graph neural networks (GNNs) have garnered significant attention due to their ability to represent graph data. Among various GNN variants, graph attention network (GAT) stands out…
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning
Ling Wang, Ye Yuan
Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network…