65 citations · 117 across the 12 of their papers we have counts for
12 papers
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,…
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
Yuxiang Ren, Bo Wang, Jiawei Zhang +1
The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media…
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…
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention
Yuxiang Ren, Jiawei Zhang
The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learn…
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…
Scalable Heterogeneous Social Network Alignment through Synergistic Graph Partition
Yuxiang Ren, Lin Meng, Jiawei Zhang
Social network alignment has been an important research problem for social network analysis in recent years. With the identified shared users across networks, it will provide resea…
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…
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…
Meta Diagram based Active Social Networks Alignment
Yuxiang Ren, Charu C. Aggarwal, Jiawei Zhang
Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fu…