A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …
A Simple Data Augmentation for Graph Classification: A Perspective of Equivariance and Invariance
In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To
address this issue, a prevailing idea is to learn stable features, on the assumption that they …
address this issue, a prevailing idea is to learn stable features, on the assumption that they …
Self-attentive Rationalization for Interpretable Graph Contrastive Learning
Graph augmentation is the key component to reveal instance-discriminative features of a
graph as its rationale–an interpretation for it–in graph contrastive learning (GCL). And …
graph as its rationale–an interpretation for it–in graph contrastive learning (GCL). And …