Advanced Graph Neural Networks

Últimos artículos

• Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification

  Camila Piscioneri Magalh\~aes, Lucas Pascotti Valem · 13 de julio de 2026

  While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming. Therefore, semi-supervised approaches such as Graph Convolutional Networks (GCNs), which learn from both labeled and unlabeled data, have emerged as a promising solution…

• Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

  Samra Sana, Giorgio Mantica, Saul Imbrici · 13 de julio de 2026

  Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centrality, formulated as a node-ranking problem. Exact centrality values are…

• Group Invariant Spectral Embedding

  Yeari Vigder, Paulina Hoyos, David Thong, Joakim and\'en, Joe Kileel, Amit Moscovich · 13 de julio de 2026

  Spectral embedding methods are widely used for dimensionality reduction and clustering of high-dimensional datasets with intrinsic low-dimensional structures. Although many datasets of practical interest exhibit invariance under symmetries such as rotations, standard spectral embedding methods do no…

• Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining

  Jan Gronewald, Andreas Emrich, Nijat Mehdiyev · 13 de julio de 2026

  Pre-demolition assessment, the regulated audit process at the heart of urban mining, is an information process in which AI support must serve qualified auditors who remain accountable for the decisions taken. The relevant unit of value is not prediction accuracy alone, but the defensibility of the s…

• Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation

  Konrad \"Ozdemir, Julia Gastinger, Lukas Kirchdorfer, Heiner Stuckenschmidt · 13 de julio de 2026

  Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time. Yet, TKG forecasting models are commonly evaluated only on empirical benchmark datasets that provide limited insight into the models' robustness to such distribu…

• Pattern-Aware Graph Neural Networks for Handling Missing Data

  Minett Tran, Taehee Jeong · 13 de julio de 2026

  Missing data is ubiquitous in real-world datasets. Traditional methods either discard incomplete samples or apply imputation techniques that ignore potentially informative missingness patterns, implicitly assuming that missingness occurs randomly. However, missingness patterns might provide addition…

• Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences

  Runyu Yu, Zhe Xue, Ang Li · 10 de julio de 2026

  In recent years, with the continuous progress of science and technology, the number of scientific research achievements has increased rapidly. As an exchange platform and medium for scientific research achievements, scientific and technological academic conferences have become increasingly abundant.…

• EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy

  Wenxiu Ding, Muzhi Liu, Zheng Yan, Mingjun Wang, Yifan Zhao, Qiao Liu · 10 de julio de 2026

  Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy edge-level differential privacy, a common approach is to inject nois…

• Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution

  Yazheng Liu, Xi Zhang, Sihong Xie, Hui Xiong · 10 de julio de 2026

  Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustworthiness of TGNs. Existing explanation methods overlook the memory module, the cor…

• Gauge-Invariant Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces

  Jiaqing Xie, Yuxin Wang · 9 de julio de 2026

  Spectral positional encodings (PEs) for \emph{directed} graphs face two obstacles: magnetic Laplacians require an $O(n^3)$ Hermitian eigendecomposition per potential, and their complex eigenvectors are defined only up to unitary gauge, which prior work handles with basis-invariant architectures. We …

• InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs

  Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah, Raghava Mutharaju · 9 de julio de 2026

  Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential First Order Logic (EFO) queries. These EFO queries contain conjunction, disjunction, and negation operators. Most existing …

• Diffusion enabled Optimal Transport distances for graph matching

  Iman Seyedi, Francesco Archetti · 9 de julio de 2026

  This paper introduces Diffusion Semi-Relaxed Fused Gromov-Wasserstein (DsrFGW), a novel method for graph comparison that unifies node features and structural connectivity through optimal transport. While traditional Gromov-Wasserstein and semi-relaxed variants (srGW, srFGW) capture graph structure, …

• Stability of Flow Models for Graph Signals

  Martin Schmidt, Gonzalo Mateos · 9 de julio de 2026

  Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While favorable stability properties of Graph Neural Networks (GNNs) have been well documented, it is unclear how structural errors propagate through the dyn…

• DiPhon: Diffusion on Graphons for Scalable Graph Generation

  Sergio Rozada, Yiming Qin, Manuel Madeira, Pascal Frossard, Alejandro Ribeiro · 9 de julio de 2026

  Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem. We approach this question in the dense-graph setting through the lens of graphons, the size-agnostic limit o…

• Any-Dimensional Learning by Sampling

  Eitan Levin, Venkat Chandrasekaran · 9 de julio de 2026

  Many machine learning models are defined for inputs of different sizes, such as point clouds containing different numbers of points, sequences of tokens of different lengths, and graphs on different numbers of nodes. Such models are trained on finitely-many examples of necessarily limited sizes. How…

• Generative Diffusion Models of Stochastic Graph Signals

  Yi\u{g}it Berkay Uslu, Samar Hadou, Sergio Rozada, Shirin Saeedi Bidokhti, Alejandro Ribeiro · 9 de julio de 2026

  Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network optimization. In these settings, the target signals are realizations of unknown conditional distributions. However, prev…

• Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation

  Zhiheng Zhou, Mengyao Zhou, Dengyi Zhao, Xingqin Qi, Guiying Yan · 9 de julio de 2026

  Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncertainty in hypergraphs arises not only from noisy attributes and ambiguous labels, but also from variations in node-hypere…

• Signed-Graph Recommendation as Structural Consistency Maximization

  Zifan Wang, Siyu Chen, Wenzhuo Song · 8 de julio de 2026

  While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by structural noise and data sparsity. In this work, we first identify a fundamental inconsistency across the structural, propagation, and semantic layers o…

• i-EXAM: Instructable and Explainable Attack Connectivity Graph Modeler

  Rakesh Podder, Wadia Ganim, Sarath Sreedharan, Indrajit Ray, Indrakshi Ray · 8 de julio de 2026

  i-EXAM is a planning-powered tool that helps system administrators to create security profiles of complex networks and perform what-if analyses to identify network hardening strategies. It leverages planning compilation that provides soundness and completeness guarantees to identify attack paths, ev…

• Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

  Vrushank Ahire, Yogesh Kumar, M. A. Ganaie · 8 de julio de 2026

  Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face challenges in preserving geometric relationships and utilizing multiple feature views effectively. To address these limitations we propose the Intu…

• Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion

  Shervin Khalafi, Igor Krawczuk, Sergio Rozada, Charilaos Kanatsoulis, Antonio G Marques, Alejandro Ribeiro · 8 de julio de 2026

  Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures like graph transformers have recently shown promise in denoising graphs. However, our principled understanding of attention-based graph denoising remains limite…

• Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)

  Kevin Xu, Alexander Quispe · 8 de julio de 2026

  GitHub hosts hundreds of millions of public repositories, but the platform exposes no native mapping from repositories to standardized industry sectors. This gap limits empirical work on the geography of innovation, the industrial composition of open-source production, and the diffusion of new techn…

• Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy

  Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu · 8 de julio de 2026

  Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods often suffer from the "structural isolation" issue during mini-batch training, ma…

• RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation

  Sambaran Bandyopadhyay, Ananth Muppidi · 8 de julio de 2026

  Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where intermediate nodes lack lexical overlap with the query. To address this, we propose…

• Parameter-Free Encoders Remain Viable for RDB Foundation Models

  Linjie Xu, David Wipf · 8 de julio de 2026

  Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest? As the space of potential targets is vast across enterprise settings, it is preferable to avoid learning a new model from scratch each time th…