Safety in graph machine learning: Threats and safeguards
Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …
years. With their remarkable ability to process graph-structured data, Graph ML techniques …
Deciphering spatio-temporal graph forecasting: A causal lens and treatment
Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
Counterfactual learning on graphs: A survey
Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …
Does invariant graph learning via environment augmentation learn invariance?
Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …
different environments for out-of-distribution generalization on graphs. As the graph …
Evaluating post-hoc explanations for graph neural networks via robustness analysis
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 …
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
A survey on explainability of graph neural networks
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have
gained significant attention and demonstrated remarkable performance in various domains …
gained significant attention and demonstrated remarkable performance in various domains …
Out-of-distribution generalization on graphs: A survey
Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …
Although booming with a vast number of emerging methods and techniques, most of the …
Exgc: Bridging efficiency and explainability in graph condensation
Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …
However, the associated computational and storage overheads raise concerns. In sight of …
Unleashing the power of graph data augmentation on covariate distribution shift
The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …
learning. From the perspective of invariant learning and stable learning, a recently well …
Brave the wind and the waves: Discovering robust and generalizable graph lottery tickets
The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …