Versatile and Privacy-Conscious Approaches in Graph Representation Learning

The recent advancements in graph representation learning have seen a shift towards more versatile and privacy-conscious approaches. Innovations in graph prompt learning (GPL) are addressing privacy risks through novel defense mechanisms, while contrastive graph condensation (CTGC) is enhancing data versatility and generalization capabilities through self-supervised learning. Additionally, rewiring techniques are being explored to mitigate structural challenges like oversquashing and oversmoothing in graph neural networks (GNNs). Notably, instance-aware graph prompt learning (IA-GPL) is advancing the field by generating distinct prompts tailored to different input instances, improving performance across diverse datasets. Educational tools like GNN 101 are also emerging to make GNN learning more accessible to non-experts through interactive visualizations.

Noteworthy Papers:

  • GraphTheft: First evaluation of privacy risks in GPL, highlighting critical vulnerabilities and defense mechanisms.
  • Contrastive Graph Condensation: Introduces CTGC to enhance cross-task generalizability through self-supervised learning.
  • Instance-Aware Graph Prompt Learning: Introduces IA-GPL to generate distinct prompts for diverse instances, outperforming state-of-the-art baselines.

Sources

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning

Hide in Plain Sight: Clean-Label Backdoor for Auditing Membership Inference

Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning

Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey

From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs

Instance-Aware Graph Prompt Learning

GNN 101: Visual Learning of Graph Neural Networks in Your Web Browser

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