22 citations · 23 across the 5 of their papers we have counts for
5 papers · 1 filter
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.…
LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity
Yuhan Chen, Yihong Luo, Jing Tang +4
Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion…
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…