65 citations · 117 across the 12 of their papers we have counts for
7 papers · 1 filter
Characterizing the Influence of Topology on Graph Learning Tasks
Kailong Wu, Yule Xie, Jiaxin Ding +4
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the…
Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Graph neural network (GNN) has shown convincing performance in learning powerful node representations that preserve both node attributes and graph structural information. However,…
Label Contrastive Coding based Graph Neural Network for Graph Classification
Yuxiang Ren, Jiyang Bai, Jiawei Zhang
Graph classification is a critical research problem in many applications from different domains. In order to learn a graph classification model, the most widely used supervision co…
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data and various tasks. However, many current GNNs suffer from three common problems…
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Yuxiang Ren, Hao Zhu, Jiawei Zhang +2
Fraud detection is extremely critical for e-commerce business. It is the intent of the companies to detect and prevent fraud as early as possible. Existing fraud detection methods…
Heterogeneous Deep Graph Infomax
Yuxiang Ren, Bo Liu, Chao Huang +3
Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used…
DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
Optimization algorithms with momentum, e.g., (ADAM), have been widely used for building deep learning models due to the faster convergence rates compared with stochastic gradient d…