6 papers · 1 filter
Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
Runlin Lei, Lu Yi, Mingguo He +4
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a comprehensive understanding of their ro…
Future Link Prediction Without Memory or Aggregation
Lu Yi, Runlin Lei, Fengran Mo +3
Future link prediction on temporal graphs is a fundamental task with wide applicability in real-world dynamic systems. These scenarios often involve both recurring (seen) and novel…
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
Lu Yi, Jie Peng, Yanping Zheng +5
Future link prediction is a fundamental challenge in various real-world dynamic systems. To address this, numerous temporal graph neural networks (temporal GNNs) and benchmark data…
Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs
Runlin Lei, Jiarui Ji, Haipeng Ding +4
With the rise of large language models (LLMs), there has been growing interest in Graph Foundation Models (GFMs) for graph-based tasks. By leveraging LLMs as predictors, GFMs have…
Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier
Lu Yi, Zhewei Wei
Graph unlearning has emerged as a pivotal research area for ensuring privacy protection, given the widespread adoption of Graph Neural Networks (GNNs) in applications involving sen…
A survey of dynamic graph neural networks
Yanping Zheng, Lu Yi, Zhewei Wei
Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, m…