2 citations · 2 across the 4 of their papers we have counts for
4 papers
TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
Longfei Ma, Zemin Liu, Fei Wu
Temporal graph learning models the evolution of dynamic systems, where both structural interactions and semantic states change over time. However, existing benchmarks primarily emp…
Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer
Wenkang Han, Wang Lin, Liya Hu +6
Knowledge tracing (KT) aims to predict learners' future performance based on historical learning interactions. However, existing KT models predominantly focus on data from a single…
A Unified Invariant Learning Framework for Graph Classification
Yongduo Sui, Jie Sun, Shuyao Wang +4
Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize sta…
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
Xingtong Yu, Zhenghao Liu, Yuan Fang +3
Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision. To reduce label…