8 papers
Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models
Fali Wang, Ali Al-Lawati, Iliyas Bektas +5
Graph reasoning provides a promising testbed for evaluating the reasoning ability of large language models (LLMs), as graph instances can be programmatically generated, structurall…
Generalizing Test-time Compute-optimal Scaling as an Optimizable Graph
Fali Wang, Jihai Chen, Shuhua Yang +7
Test-Time Scaling (TTS) improves large language models (LLMs) by allocating additional computation during inference, typically through parallel, sequential, or hybrid scaling. Howe…
Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs
Yilong Wang, Tianxiang Zhao, Zongyu Wu +1
Graph neural networks (GNNs) have shown great ability for node classification on graphs. However, the success of GNNs relies on abundant labeled data, while obtaining high-quality…
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning
Yilong Wang, Jiahao Zhang, Tianxiang Zhao +1
Despite their impressive predictive performance, GNNs often exhibit poor confidence calibration, i.e., their predicted confidence scores do not accurately reflect true correctness…
Enhance Graph Alignment for Large Language Models
Haitong Luo, Xuying Meng, Suhang Wang +4
Graph-structured data is prevalent in the real world. Recently, due to the powerful emergent capabilities, Large Language Models (LLMs) have shown promising performance in modeling…
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
Quan Li, Tianxiang Zhao, Lingwei Chen +2
Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classificatio…