7 accepted papers
Graph neural networks (GNNs) have demonstrated strong performance in various graph mining tasks but rely heavily on extensively labeled nodes. To improve training efficiency, graph active learning (GAL) has emerged as a solution for selecting the most informative nodes for labeling. However, existin
Large Language Models (LLMs) show remarkable potential for few-shot information extraction (IE), yet their performance is highly sensitive to the choice of in-context examples. Conventional selection strategies often fail to provide informative guidance, as they overlook a key source of model fallib
Class imbalance in graph data presents a significant challenge for effective node classification, particularly in semi-supervised scenarios. In this work, we formally introduce the concept of geometric imbalance, which captures how message passing on class-imbalanced graphs leads to geometric ambigu…
Large language models have demonstrated considerable capabilities in various mathematical tasks, yet they often fall short in rigorous, proof-based reasoning essential for research-level mathematics. Retrieval-augmented generation presents a promising direction for enhancing these capabilities. This
Graph Neural Networks (GNNs) are powerful tools for processing graph data, but training them is typically time-consuming and expensive in terms of GPU memory. Recently, graph reduction techniques have been investigated as a pre-processing step, due to the fact that the time and space complexity of G…
Graph contrastive learning (GCL) has become a powerful tool for learning graph data, but its scalability remains a significant challenge. In this work, we propose a simple yet effective training framework called Structural Compression (StructComp) to address this issue. Inspired by a sparse low-rank…
This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance Decomposition, establishing a theoretical framework that closely relates data i…