This paper addresses the challenge of out-of-distribution (OOD) generalization in graph
machine learning, a field rapidly advancing yet grappling with the discrepancy between …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Abstract In recent years, Graph Neural Networks (GNNs), an emerging technology for
learning from graph-structured data, have attracted much attention. Despite the widespread …

Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-
supervised node classification tasks. However, recent studies have revealed various biases …

While the state-of-the-art network embedding approaches often learn high-quality
embeddings for high-degree nodes with abundant structural connectivity, the quality of the …

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown
state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show …

Recent studies successfully learned static graph embeddings that are structurally fair by
preventing the effectiveness disparity of high-and low-degree vertex groups in downstream …

We study deceptive fairness attacks on graphs to answer the following question: How can
we achieve poisoning attacks on a graph learning model to exacerbate the bias …

Like Graph Neural Networks (GNNs), Signed Graph Neural Networks (SGNNs) are also up
against fairness issues from source data and typical aggregation method. In this paper, we …