12 citations · 18 across the 6 of their papers we have counts for
6 papers
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…
BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization
Jiyang Bai, Yuxiang Ren, Jiawei Zhang
For various optimization methods, gradient descent-based algorithms can achieve outstanding performance and have been widely used in various tasks. Among those commonly used algori…
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…
LATTE: Application Oriented Social Network Embedding
Lin Meng, Jiyang Bai, Jiawei Zhang
In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However,…