Graph Neural Networks

GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms …
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GOODAT: Towards Test-time Graph Out-of-Distribution Detection
Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in …
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NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped facts. Recent advancements have been explored to enhance …
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Towards Model Extraction Attacks in GAN-based Image Translation via Domain Shift Mitigation
Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim deep neural network (DNN) model by only …
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as …
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Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, …
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Towards Self-Interpretable Graph-Level Anomaly Detection
Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a …
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A Comprehensive Survey on Distributed Training of Graph Neural Networks
Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their …
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PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various …
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Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection
Multivariate time-series anomaly detection is critically important in many applications, including retail, transportation, power grid, …
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