With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …

Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …

The discovery of novel structural classes of antibiotics is urgently needed to address the
ongoing antibiotic resistance crisis,,,,,,,–. Deep learning approaches have aided in exploring …

We consider the problem of explaining the predictions of graph neural networks (GNNs),
which otherwise are considered as black boxes. Existing methods invariably focus on …

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input
graph's features--rationale--which guides the model prediction. Unfortunately, the leading …

Graph representation learning has shown effectiveness when testing and training graph
data come from the same distribution, but most existing approaches fail to generalize under …

This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …

Despite recent success in using the invariance principle for out-of-distribution (OOD)
generalization on Euclidean data (eg, images), studies on graph data are still limited …