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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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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 …
IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and …
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 …
Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
Large language models (LLMs) have demonstrated impressive reasoning abilities in complex tasks. However, they lack up-to-date knowledge …
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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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Unifying Large Language Models and Knowledge Graphs: A Roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial …
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Maximizing Malicious Influence in Node Injection Attack
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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