Advanced Graph Neural Networks

Derniers papiers

• Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs

  Aniq Ur Rahman · 29 juin 2026

  Temporal link prediction is usually evaluated by predictive performance on unseen edges, but in probabilistic temporal graphs this criterion can conflate model error with irreducible uncertainty. We study this issue by characterising an inherent estimation--prediction tradeoff in binary logistic mod…

• Understanding Rollout Error in Graph World Models

  Xinyuan Song, Zekun Cai · 29 juin 2026

  World models are often used for planning by rolling learned dynamics forward. Many planning environments, however, are not vectors or images; they are graphs of agents, tools, skills, routes, and dependencies. In these settings, a local prediction error may stay local or spread through the graph, an…

• KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems

  Minjun Choi, Yerin Kim, Junghyuk Seo, Sujin Mo, Hyemin Lee, Youngjoong Ko · 29 juin 2026

  Enterprise Knowledge Graphs (KGs) are increasingly used for internal search, analytics, and question answering, but building natural-language interfaces for private enterprise graphs remains costly. We present KG2Cypher, a data-centric pipeline for building enterprise text-to-Cypher systems from exi…

• RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion

  Yike Liu, Peijia Xie, Chao He, Huiling Zhu · 29 juin 2026

  Real-world knowledge graphs are often incomplete, lacking many valid facts. Knowledge Graph Completion (KGC) aims to predict missing links using known triples, thereby enhancing graph coverage. A key challenge is modeling diverse relational patterns such as symmetry, antisymmetry, inversion, composi…

• TeRoR: Decoupled Temporal Rotation with Relational Circular Region for Temporal Knowledge Graph Embedding

  Peijia Xie, Yike Liu, Chao He, Huiling Zhu · 29 juin 2026

  In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding methods. TeRo is a simple and efficient temporal knowledge graph embedding appr…

• Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection

  Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi · 29 juin 2026

  Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks. Real-world fraud graphs pose two coupled challenges: sparse and imbalanced supervision, where verified fraudulent labels …

• AI-Model Network: Concept, Current State and Future

  Li Zhetao, Zeng Xiyu, Wang Jianhui, Xiao Yong, Liu Zhongren, Wu Junru, Lai Junjie, Huang Jijun, Long Saiqin · 29 juin 2026

  While the primary function of computers lies in computation and processing, the core value of the Internet is rooted in sharing and collaboration. Computers create the Internet, and the Internet empowers the value of computers. The rapid development of the Internet, cloud computing, and big data is …

• Directed Graph Topology Inference via Graph Filter Identification

  Rasoul Shafipour, Andrei Buciulea, Santiago Segarra, Antonio G. Marques, Gonzalo Mateos · 29 juin 2026

  We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs of a graph convolutional filter, i.e., a polynomial (with unknown coefficients) of a local diffusion graph-shift operator…

• Graph Neural Networks Applications Across Domains: All Insights You Need

  Abderaouf Bahi · 26 juin 2026

  Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The interesting question is no longer whether message passing helps on a given dataset, but where graph structure earns its computational cost and wher…

• Explaining Temporal Graph Neural Networks via Feature-induced Information Flow

  Ping Xiong, Thomas Schnake, Klaus-Robert M\"uller, Shinichi Nakajima · 26 juin 2026

  Event-based Temporal Graph Neural Networks (ETGNNs) have demonstrated strong performance across a wide range of applications, including social network analysis, epidemic tracing, recommender systems, and political event forecasting. However, their increasing complexity poses significant challenges f…

• Zero-Shot Size Transfer for Neural ODEs on Sparse Random Graphs: Graphon Limits and Adjoint Convergence

  Mingsong Yan, Zhida Wang, Sui Tang · 26 juin 2026

  Graph Neural Differential Equations (GNDEs) model continuous-time graph dynamics by parameterizing Neural ODE velocity fields with Graph Neural Networks. Their local, size-independent filters suggest a zero-shot size-transfer principle: train on a small graph and deploy on larger, similar graphs wit…

• A General Framework for Learning Algebraic Properties from Cayley Graphs using Graph Neural Networks

  Tal Weissblat · 26 juin 2026

  A Graph Neural Network (GNN) framework for predicting the solvability of finite groups from their Cayley graph representations was introduced in [1]. In the present work, we generalize this approach and develop a property-independent framework for learning algebraic properties of finite groups direc…

• Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture

  Xiang Li, Shanshan Wang, Chenglong Xiao · 26 juin 2026

  The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances. This underscores the critical need for instance-a…

• Retrieval-Warmed Energy-Based Reasoning: A Five-Arm Ablation Methodology for Diffusion-as-Inference on Structured Reasoning Tasks

  Libo Sun, Po-Wei Harn, Zewei Zhang, Peixiong He, Xiao Qin · 26 juin 2026

  Warm-started diffusion samplers accelerate iterative inference, but it is rarely clear which part of the pipeline carries the gain. We study \textbf{retrieval-warmed energy-based reasoning (RW-EBR)} -- an IRED energy-based diffusion model \cite{du2024ired} augmented with a Modern Hopfield trajectory…

• Closed-Loop Graph Algorithm Execution with Small Language Models: Step Accuracy and Rollout Reliability

  Michal Podstawski · 25 juin 2026

  Small language models offer an efficient alternative to large-scale systems, but their ability to execute structured algorithms over multiple dependent decisions remains poorly understood. We study graph algorithm execution as a closed-loop prediction problem in which a model repeatedly selects the …

• Communicability-Inspired Positional Encoding (CIPE)

  Yipeng Zhang, Zhongtian Sun, Pietro Li\`o, Kelin Xia · 25 juin 2026

  Positional encodings (PEs) are essential for Transformers. Yet designing effective PEs for non-Euclidean graphs remains challenging. Such encodings should ideally induce an Attention-Compatible Geometry for self-attention: not merely describing graph structure, but defining a geometry whose inner pr…

• A Framework for Directed Hypergraph Signal Processing via tensor t-SVD

  Carlos Mundo-Levano, Nicol\'as Bello, Daniel L. Lau, Gonzalo R. Arce · 25 juin 2026

  We introduce Directed Hypergraph Signal Processing (DHGSP), a unified framework that extends graph signal processing to accommodate both higher-order (polyadic) and asymmetric (directional) relationships simultaneously. Using the tensor singular value decomposition (t-SVD) within the t-product algeb…

• CausalRAG2: Hierarchical Causal Knowledge Graph Design for RAG

  Nengbo Wang, Tuo Liang, Vikash Singh, Chaoda Song, Van Yang, Yu Yin, Jing Ma, Jagdip Singh, Vipin Chaudhary · 25 juin 2026

  Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structured retrieval and reasoning. However, existing graph-based methods often over-rely on entity-centric node matching and lac…

• Fuzzy Quantification over OWL Ontologies and Knowledge Graphs

  Enrique Palac\'in, Fernando Bobillo, Ignacio Huitzil, Francesca A. Lisi, Umberto Straccia · 25 juin 2026

  This paper presents a versatile framework for evaluating fuzzy quantification queries over both standard and fuzzy ontologies as well as knowledge graphs. The primary objective is the retrieval of individuals that satisfy queries articulated via Type I or Type II fuzzy quantified expressions. A key …

• Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks

  Ranhui Yan, Jia Cai, Mengzhu Chen, Haodong Yang · 25 juin 2026

  Spectral graph neural networks (GNNs) interpret message passing as frequency-selective filtering. While low-order spectral filters are efficient, their limited selectivity often leads to weak attenuation outside the passband, whereas high-order alternatives introduce optimization challenges. We prop…

• Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

  Long Chen, Ryan Razkenari, Yuxuan Zhou, Yuan Tian, Rahul Ghosh, Venkatesh Pappakrishnan, Disha Ahuja, Vidya Sagar Ravipati · 25 juin 2026

  As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We p…

• Holographic Memory for Zero-Shot Compositional Reasoning in Knowledge Graphs: A Mechanistic Study of Where and Why It Fails

  Randhir Kumar · 25 juin 2026

  Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whose relation chains never appeared during training. Holographic Reduced Representations (HRR), which bind and unbind symbols via circular convolution,…

• A Fair Evaluation of Graph Foundation Models for Node Property Prediction

  Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev, Liudmila Prokhorenkova · 24 juin 2026

  Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention. While many different types of models are called GFMs, particular interest has been paid to GFMs designed for node pro…

• Repeated Shared Access Enables Grokking, but Edit Propagation Depends on an Addressable Memory

  Yanan Niu · 24 juin 2026

  We study factual edit propagation in a controlled synthetic knowledge-graph QA setting using a 2x2 grid that crosses loop recurrence with shared-memory access: a dense transformer (Dense), a looped transformer (Loop), a dense backbone with shared memory (Dense+Mem), and a looped backbone with shared…

• Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules

  Nasim Shirvani-Mahdavi, Chengkai Li · 24 juin 2026

  Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs. This work presents Rule2Text, a comprehensive framework that lev…