Search Results for author:
Found 4 papers, 1 papers with code
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
GUARD: Graph Universal Adversarial Defense
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
