57 citations · 80 across the 6 of their papers we have counts for
6 papers
Understanding Heterophily for Graph Neural Networks
Junfu Wang, Yuanfang Guo, Liang Yang +1
Graphs with heterophily have been regarded as challenging scenarios for Graph Neural Networks (GNNs), where nodes are connected with dissimilar neighbors through various patterns.…
Common Knowledge Learning for Generating Transferable Adversarial Examples
Ruijie Yang, Yuanfang Guo, Junfu Wang +2
This paper focuses on an important type of black-box attacks, i.e., transfer-based adversarial attacks, where the adversary generates adversarial examples by a substitute (source)…
Heterophily-Aware Graph Attention Network
Junfu Wang, Yuanfang Guo, Liang Yang +1
Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculat…
Binary Graph Convolutional Network with Capacity Exploration
Junfu Wang, Yuanfang Guo, Liang Yang +1
The current success of Graph Neural Networks (GNNs) usually relies on loading the entire attributed graph for processing, which may not be satisfied with limited memory resources,…
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
Junfu Wang, Yuanfang Guo, Liang Yang +1
Graph Neural Networks (GNNs) have been generalized to process the heterogeneous graphs by various approaches. Unfortunately, these approaches usually model the heterogeneity via va…
Bi-GCN: Binary Graph Convolutional Network
Junfu Wang, Yunhong Wang, Zhen Yang +2
Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into…