Scalability and Generalization in Graph Neural Networks

Enhanced Scalability and Generalization in Graph Neural Networks

Recent advancements in Graph Neural Networks (GNNs) have significantly focused on enhancing scalability and generalization capabilities, addressing critical challenges such as cold-start recommendations, few-shot node classification, and anomaly detection across diverse datasets. Innovations in GNN architectures, such as the integration of attention mechanisms and self-supervised learning, have shown promise in improving both performance and computational efficiency. Additionally, the introduction of novel training paradigms like Sharpness-Aware Minimization (SAM) variants and graph pre-training models has demonstrated superior anomaly detection capabilities, particularly under limited supervision. These developments collectively underscore a shift towards more versatile and efficient GNN frameworks that can handle a broader range of graph-based tasks, from node classification to graph-level anomaly detection.

Noteworthy Developments:

  • Graph Neural Patching for Cold-Start Recommendations: Introduces a dual-functional GNN framework that excels in both warm and cold user/item recommendations.
  • Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts: Proposes a novel zero-shot GAD approach that generalizes across datasets without retraining.
  • Fast Graph Sharpness-Aware Minimization for Few-Shot Node Classification: Integrates SAM into GNN training, significantly reducing computational costs while enhancing generalization.
  • Graph Pre-Training Models Are Strong Anomaly Detectors: Demonstrates the superior performance of graph pre-training models in anomaly detection, especially under limited supervision.

Sources

Graph Neural Patching for Cold-Start Recommendations

Learning to Control the Smoothness of Graph Convolutional Network Features

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

Faster Inference Time for GNNs using coarsening

Deep Graph Attention Networks

Focus Where It Matters: Graph Selective State Focused Attention Networks

Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification

Learning Graph Filters for Structure-Function Coupling based Hub Node Identification

Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks

Bonsai: Gradient-free Graph Distillation for Node Classification

Graph Pre-Training Models Are Strong Anomaly Detectors

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