graph neural networks

Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging …
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Securing Graph Neural Networks in MLaaS: A Comprehensive Realisation of Query-based Integrity Verification
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Cost-effective Data Labelling for Graph Neural Networks
Active learning (AL), that aims to label limited data samples to effectively train the model, stands as a very cost-effective data …
Online GNN Evaluation Under Test-time Graph Distribution Shifts
Evaluating the performance of a well-trained GNN model on real-world graphs is a pivotal step for reliable GNN online deployment and …
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike …
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Maximizing Malicious Influence in Node Injection Attack
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 …