3 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
Network Tomography with Path-Centric Graph Neural Network
Yuntong Hu, Junxiang Wang, Liang Zhao
Network tomography is a crucial problem in network monitoring, where the observable path performance metric values are used to infer the unobserved ones, making it essential for ta…
GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
Junxiang Wang, Liang Zhao
We introduce GraphSL, a new library for studying the graph source localization problem. graph diffusion and graph source localization are inverse problems in nature: graph diffusio…
Non-Euclidean Spatial Graph Neural Network
Zheng Zhang, Sirui Li, Jingcheng Zhou +4
Spatial networks are networks whose graph topology is constrained by their embedded spatial space. Understanding the coupled spatial-graph properties is crucial for extracting powe…
Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First
Zheng Zhang, Junxiang Wang, Liang Zhao
Graph Neural Networks (GNNs) have achieved great success in representing data with dependencies by recursively propagating and aggregating messages along the edges. However, edges…
Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework
Junxiang Wang, Hongyi Li, Zheng Chai +3
While Graph Neural Networks (GNNs) are popular in the deep learning community, they suffer from several challenges including over-smoothing, over-squashing, and gradient vanishing.…