The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

Employers are adopting algorithmic hiring technology throughout the recruitment pipeline.
Algorithmic fairness is especially applicable in this domain due to its high stakes and …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

Fairness-aware graph learning has gained increasing attention in recent years.
Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different …

Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties
and apply them to different downstream tasks. Similar to pre-trained language models …

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 …

In the current era of rapidly growing digital data, evaluating the political bias and factuality of
news outlets has become more important for seeking reliable information online. In this …

In recent years, Graph Neural Networks (GNNs) have made significant advancements,
particularly in tasks such as node classification, link prediction, and graph representation …

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream
task execution by retrieving additional information, such as knowledge, skills, and tools from …

Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from
financial fraud detection to fake news detection. However, current GAD methods largely …