Advanced Graph Neural Networks

Últimos artículos

• Integrating Implicit and Explicit Relational Biases through Graph-Based Multiple Instance Learning: A Case Study in Skin Lesion Diagnosis

  Rafa{\l} Buler (Gda\'nsk University of Technology), Jakub Buler (Gda\'nsk University of Technology), Maciej Bobowicz (Medical University of Gda\'nsk), Micha{\l} Grochowski (Gda\'nsk University of Technology) · 7 de agosto de 2026

  Relational inductive biases are essential for capturing structural dependencies among data. This study investigates a dual-level relational framework for image classification, bridging the gap between implicit representation learning and explicit structural modelling. We begin by establishing a base…

• Shapes from Examples: Foundations of Shape Learning in Recursive SHACL

  Bente Gortworst, Cem Okulmus, Magdalena Ortiz, Anni-Yasmin Turhan · 7 de agosto de 2026

  SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known fitting approach to this task: given sets P and N of positive and negative example nodes from an input graph, compute a shape expression C, possibly us…

• Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts

  Quanxin Wang, Xuanting Xie, Bingheng Li, Xingtong Yu, Shuo Wang, Ruiyi Fang, Zhao Kang · 7 de agosto de 2026

  Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a single, fixed embedding space. In this work, we reveal that temporal shifts in local clustering and degree heterogeneit…

• iStructTab: Structured Feature Sequencing for Multimodal Learning of Image and Tabular Data

  Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali, Gianfranco Doretto, Donald A. Adjeroh · 6 de agosto de 2026

  Multimodal learning of images and tabular data is often impaired by ineffective representations, resulting in redundancy, dispersion, and generalization problems. To tackle this challenge, we introduce Graph-Enhanced Descriptor Sequencing (GEDS), a structured feature sequencing algorithm grounded in…

• NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

  Tinghe Zhang, Jian Xu, Jiaheng Chen, Jiaxing Li, Yucheng Xiao, Qiang Wang · 6 de agosto de 2026

  Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space. Both paradigms can entangle representations with low-level input statistics rather than with rel…

• Towards Trustworthy Hypergraph Neural Networks under Label Noise

  Mengyao Zhou, Zhiheng Zhou, Xiao Han, Guiying Yan · 6 de agosto de 2026

  Hypergraph neural networks (HGNNs) have demonstrated remarkable capabilities in processing complex higher-order relationships. However, their performance is highly dependent on labeled data, making them vulnerable to label noise. Despite advances in learning with label noise (LLN) and graph learning…

• PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration

  Yuyang Xia, Ruixuan Liu, Li Xiong · 6 de agosto de 2026

  Graph inference over relational data can expose sensitive edge information, and this risk becomes more severe in dynamic graphs, where repeated model updates cause privacy loss to accumulate. We formulate Edge-level Differentially Private Dynamic Graph Inference (EDG) and propose PriDyG, a private i…

• When Should Graph Attention Be Sparse? Learning a Per-Edge Tsallis Index

  Kleyton da Costa, Bernardo Modenesi · 5 de agosto de 2026

  Graph attention normalizes neighborhood scores with softmax, the maximum-entropy choice under Shannon statistics. But homophilic and heterophilic graphs want different attention shapes, and one fixed normalization cannot serve both. We propose \textbf{LTGA} (\textbf{L}earnable \textbf{T}sallis \text…

• A Graph Signal Processing Perspective on Numerical Sequence Representations in LLM In-Context Learning

  Jiajun Bao, Zihao Qi, Toni J. B. Liu, Gurbir Arora, Rapha\"el Sarfati, Nicolas Boull\'e, Christopher J. Earls · 5 de agosto de 2026

  Pretrained large language models (LLMs) have demonstrated in-context learning (ICL) capabilities for numerical inference over sequences serialized as text. Prior work has identified and characterized this form of numerical inference primarily through output-level evaluations such as prediction error…

• CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting

  Julia Gastinger, Christian Meilicke, Heiner Stuckenschmidt · 4 de agosto de 2026

  We address the task of temporal knowledge graph forecasting with an inherently interpretable method based on symbolic rules. Motivated by recent work proposing a strong baseline based on recurrent facts, our approach learns four simple rule types, including temporal rules with confidence functions t…

• Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

  Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan · 4 de agosto de 2026

  Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains. Federated multimodal graph learning (FMGL) extends federated graph learning (FGL) to MAGs, ena…

• Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

  Xin Liu, Xiyuan Chen, Chenglong Wu, Xuan Zong, Jun Zhou, Dawei Cheng · 4 de agosto de 2026

  Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financia…

• Benchmarking Sheaf Neural Networks for Inductive Tasks

  Stefano Fiorini, Edoardo Coppola, Pietro Li\`o · 4 de agosto de 2026

  Sheaf Neural Networks (SNNs) generalize message passing by replacing scalar edge weights of standard Graph Neural Networks (GNNs) with learnable, edge-dependent restriction maps between node stalks. Despite their strong theoretical foundations and promising transductive results, SNNs have been evalu…

• Agentic Graph Token Reasoning

  Zhuoyi Peng, Yi Yang · 4 de agosto de 2026

  Graphs model relational data throughout science and industry, from citation networks to product co-purchase graphs. Because the nodes of many such graphs carry rich text, a growing line of work applies large language models (LLMs) to graph analysis. The most graph-native of these methods use graph t…

• Differentiable Lifting for Topological Neural Networks

  Jorge Luiz Franco, Gabriel Duarte, Alexander Nikitin, Moacir Ponti, Diego Mesquita, Amauri H. Souza · 4 de agosto de 2026

  Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.g., cycles and cliques) to boost the expressive power of message-passing neural networks. In turn, however, these structures are typically identified a priori through an unsupervised graph lifting operation. Notw…

• Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification

  V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov · 4 de agosto de 2026

  We propose Kohn-Sham Spectral Embedding (KSSE), an energy-based model replacing the dense classifier of convolutional neural networks with a sparse-graph spectral embedding evaluated at the Nishimori temperature of an associated Random-Bond Ising Model (RBIM). Mapping pre-trained features onto quasi…

• HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

  Ruichen Xu, Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Linsey Pang, Ravid Shwartz-Ziv, Yann LeCun, Yuefan Deng · 4 de agosto de 2026

  Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input reconstruction by predicting masked targets directly in latent space. However, exi…

• Nonlinear Laplacians Improve Signed-Directed Graph Learning

  Ali Parviz, Yuichi Yoshida · 4 de agosto de 2026

  While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We introduce a non-linear Laplacian operator specific to signed and directed networks …

• CoRe-GNN: Multilevel Message passing on Coarsened graphs

  Antonin Joly, Nicolas Keriven, Aline Roumy · 4 de agosto de 2026

  Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes …

• Fairness in Augmented Graph Learning: A Survey

  Renqiang Luo, Huafei Huang, Ziqi Xu, Xikun Zhang, Enyan Dai, Bo Yang, Feng Xia · 4 de agosto de 2026

  Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques. Examples include federated learning, graph transformers, and graph condensation. While enhancing model utility, AGL introduces unique intersectional fairness challenges that tr…

• Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models

  Yi Wang, Jitao Zhao, Di Jin, Dongxiao He · 3 de agosto de 2026

  Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains. Unlike traditional graph learning methods that are typically designed for in-domain settings, GFMs aim to learn transferable knowledge that can generalize to unseen gr…

• Cross-Resolution Semantic Learning for Graph Domain Adaptation

  Yingxu Wang, Haoze Huang, Zhongkai Zheng, Shangsong Liang · 3 de agosto de 2026

  Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing methods align representations or regularize graph structures, but do not explicitly model how class-discriminative knowledge learned at different sour…

• Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction

  Sen Zhao, Cheng Liu, Shuyin Xia, Zhiyuan Liu, Yi Liu, Yi Wang, Wei Wang · 3 de agosto de 2026

  Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction, as it distinguishes homogeneous nodes through their relative relationships, facilitating the accurate capture of structural patterns and implicit co…

• AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control

  Jingbo Cui, Jitao Zhao, Di Jin, Dongxiao He · 30 de julio de 2026

  Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has long been central to GFM research. However, local structural patterns …

• ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions

  Yule Li, Yifeng Lu, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding · 30 de julio de 2026

  In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode specific inductive biases (e.g., acting as low-pass or high-pass filters), most existing methods implicitly assume conditional independence among node labe…