graph-learning

The paper proposes Label Influence Propagation (LIP), a model that analyzes and propagates label influences in graph neural networks for multi-label node classification, consistently outperforming state-of-the-art methods.

Proposes SAOT, a structure-aware optimal transport framework for self-supervised continual graph learning that preserves relational structure across tasks. Achieves significant performance gains over state-of-the-art methods on multiple benchmarks, including up to 15% improvement on Products-CL.

TAG-DLM unifies textual reasoning and graph message passing within a masked diffusion language model, enabling joint reasoning over text and graph topology for node classification and link prediction tasks.

This paper introduces GRAFT, a curated multimodal dataset linking gene expression profiles and phenotypic traits in Arabidopsis thaliana, along with graph and hypergraph benchmarks for phenotype prediction. It aims to advance genome-to-phenome mapping in plant biology.

Proposes DCQ-GNN, a spectral GNN that uses a compact bank of adaptive convex-concave quadratic filters to improve spectral selectivity without high-order polynomials, achieving competitive results on both homophilic and heterophilic graphs.

This paper introduces FedEPD, a framework for federated graph learning under long-tailed data distributions. It uses an energy-guided dual decoupling approach to separate topological purification from semantic recalibration, achieving state-of-the-art performance on benchmarks with up to 4.97% accuracy improvement.

FoundCause is an amortized causal discovery model that explicitly handles latent confounders and missing data, outperforming 15 existing methods on real-world datasets with a single forward pass.

This paper proposes LLM-GNN Co-Teaching, a bidirectional framework for few-shot graph learning on text-attributed graphs. The LLM and GNN exchange confident pseudo-labels and use round-based preference optimization (RPL-PO) to mutually improve, outperforming prior methods on benchmarks.

This survey reviews the use of large language models for graph computation, categorizing them into two paradigms: LLMs as executors and LLMs as planners. It finds LLMs promising for simple tasks but unreliable for large-scale exact computations, and suggests future directions.

This paper analyzes how large language models internally process graph tokens in Graph Language Models (GLMs), finding a decoupling between activation-level saliency and graph-semantic utility. Graph sink tokens emerge as activation outliers but are not the primary carriers of graph structure, revealing limitations in current graph-token construction and alignment mechanisms.

This paper presents a graph-learning-aided optimization approach for designing active tether-net systems to capture space debris, using a GNN to recommend candidate designs and reduce mixed-combinatorial nonlinear programming to standard NLP problems, achieving faster convergence.

This paper introduces GraphReAct, a framework that extends reasoning-acting paradigms to graph-structured data for multi-step inference. It combines topological and semantic retrieval with context refinement to improve performance on graph learning benchmarks.