LLaGA: A Versatile Framework for Integrating Large Language Models with Graph-Structured Data
LLaGA is a novel framework that seamlessly integrates the capabilities of Large Language Models (LLMs) with graph-structured data, enabling versatile and generalized performance across various graph tasks.
Efficient Graph Distillation for Graph Classification using Computation Tree Patterns
MIRAGE, a novel graph distillation algorithm, exploits the skewed distribution of computation trees in graphs to condense the training data without compromising model performance. MIRAGE is architecture-agnostic and computationally efficient, outperforming state-of-the-art baselines in accuracy, compression, and distillation speed.
Probing the Limitations and Ethical Implications of the Weisfeiler-Leman Test for Measuring Graph Neural Network Expressive Power
The Weisfeiler-Leman (WL) test is commonly used to measure the expressive power of graph neural networks, but this approach has significant limitations and ethical implications that are often overlooked.
Exploring the Capabilities and Limitations of Graph Transformers: A Comprehensive Taxonomy and Empirical Study
Graph transformers have emerged as a promising alternative to graph neural networks, but their theoretical properties and practical capabilities require deeper understanding. This work provides a comprehensive taxonomy of graph transformer architectures, analyzes their theoretical properties, and empirically evaluates their ability to capture graph structure, mitigate over-smoothing, and alleviate over-squashing.
Self-Guided Robust Graph Structure Refinement: Defending Against Adversarial Attacks
The author proposes a self-guided GSR framework to enhance robustness against adversarial attacks by utilizing a clean sub-graph and addressing technical challenges. The approach outperforms existing methods under various attack scenarios.
Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments
The author proposes a novel method to generate hierarchical semantic environments for each graph, enhancing graph invariant learning by considering relationships between environments and maintaining consistency across different hierarchies.
