However, the evaluation process presents substantial challenges.

Consequently, recent research efforts have focused on developing pre-trained TS forecasting models.

Membership inference arises in these contexts as a potential auditing method for detecting unauthorized data usage.

Training (AL T), which aims to enhance LLMs' reasoning capabilities by program-generated logical reasoning samples.

Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when facing malicious adversarial attacks. However, all existing fairness attacks require manipulating the connectivity between existing nodes, which may be prohibited in reality. To this end, we introduce a N ode I njection-based F airness A ttack (NIFA), exploring the vulnerabilities of GNN fairness in such a more realistic setting. In detail, NIFA first designs two insightful principles for node injection operations, namely the uncertainty-maximization principle and homophily-increase principle, and then optimizes injected nodes' feature matrix to further ensure the effectiveness of fairness attacks. Comprehensive experiments on three real-world datasets consistently demonstrate that NIFA can significantly undermine the fairness of mainstream GNNs, even including fairness-aware GNNs, by injecting merely 1% of nodes. We sincerely hope that our work can stimulate increasing attention from researchers on the vulnerability of GNN fairness, and encourage the development of corresponding defense mechanisms.

For what purpose was the dataset created? To address this, we propose ViLCo-Bench. Who created the dataset(e.g., which team, research group) and on behalf of which Who funded the creation of the dataset? What do the instances that comprise the dataset represent (e.g., documents, photos, What data does each instance consist of? Is there a label or target associated with each instance?

This field is a relatively under-explored area, and establishing appropriate datasets is crucial for facilitating communication and research in this field.