graph-learning

This paper proposes a graph dictionary learning framework that represents graphs as Gaussian distributions using filtered Laplacians and optimal transport distances, achieving competitive performance in graph clustering and classification tasks.

This paper proposes SLM-Conditioned Hierarchical Relation Routing, an architecture that integrates small language models into graph neural networks to enable adaptive message selection in labeled property graphs, enhancing prediction accuracy by leveraging contextual semantic information.

This paper proposes MS-WDRO, a multi-source Wasserstein distributionally robust graph learning framework that fuses heterogeneous sources via Wasserstein barycenter and minimizes worst-case risk for robust graph topology inference, outperforming baselines in graph recovery and diagnostic utility.

This paper introduces AutoGrable, a method that scores candidate graph constructions for tabular data without training a GNN, using a label-alignment risk based on 1-WL color refinement, enabling cheap search for effective table-to-graph mappings.

This paper proposes PairAlign, a pair-centric graph rewiring framework that uses optimal transport-guided communication alignment to alleviate over-squashing in message-passing neural networks, with theoretical analysis and experiments on standard benchmarks.

This paper introduces NodeJEPA, a joint-embedding predictive architecture for node-level graph self-supervised learning that predicts latent representations of masked structure-aware ego-subgraphs, avoiding reconstruction and hand-crafted augmentations. The method is evaluated on node classification benchmarks and shows competitive performance.

This paper systematically studies hypergraph node classification under label noise, proposing HyperTrust, a robust framework with HyperedgeBoost and HyperedgePrune modules, along with a unified benchmark for evaluating LLN and GLN methods on hypergraphs.

Proposes FedTCR, the first systematic federated multimodal graph learning algorithm that handles task, modality, and topology heterogeneity via topology-aware cross-modal routing and tri-level contrastive learning, outperforming baselines across 7 domains.

This paper studies temporal knowledge graph forecasting under controlled distribution shifts using a synthetic generator that encodes recurrence, homophily, and periodicity. Experiments on seven architectures reveal signal-dependent robustness and limitations in model adaptivity to structural breaks.

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.