Advanced Graph Neural Networks

Derniers papiers

• Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction

  Van Thuy Hoang, O-Joun Lee · 19 janvier 2026

  Molecular property prediction is becoming one of the major applications of graph learning in Web-based services, e.g., online protein structure prediction and drug discovery. A key challenge arises in few-shot scenarios, where only a few labeled molecules are available for predicting unseen properti…

• Feature Propagation on Knowledge Graphs using Cellular Sheaves

  John Cobb, Thomas Gebhart · 19 janvier 2026

  Many inference tasks on knowledge graphs, including relation prediction, operate on knowledge graph embeddings -- vector representations of the vertices (entities) and edges (relations) that preserve task-relevant structure encoded within the underlying combinatorial object. Such knowledge graph emb…

• Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud

  Rongkun Cui, Nana Zhang, Kun Zhu, Qi Zhang · 19 janvier 2026

  Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance. Such threats have become a significant web harm that erodes societal fairness and …

• Deep GraphRAG: A Balanced Approach to Hierarchical Retrieval and Adaptive Integration

  Yuejie Li, Ke Yang, Tao Wang, Bolin Chen, Bowen Li, Chengjun Mao · 19 janvier 2026

  Graph-based Retrieval-Augmented Generation (GraphRAG) frameworks face a trade-off between the comprehensiveness of global search and the efficiency of local search. Existing methods are often challenged by navigating large-scale hierarchical graphs, optimizing retrieval paths, and balancing explorat…

• Superposition in Graph Neural Networks

  Lukas Pertl, Han Xuanyuan, Pietro Li\`o · 19 janvier 2026

  Interpreting graph neural networks (GNNs) is difficult because message passing mixes signals and internal channels rarely align with human concepts. We study superposition, the sharing of directions by multiple features, directly in the latent space of GNNs. Using controlled experiments with unambig…

• CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs

  Yuanxiang Liu, Songze Li, Xiaoke Guo, Zhaoyan Gong, Qifei Zhang, Huajun Chen, Wen Zhang · 19 janvier 2026

  Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations. While Knowledge Graphs (KGs) offer explicit grounding, existing paradigms of KG-augmented LLMs typically exhibit cognitive rigidity--applying homogeneou…

• Aggregating Direct and Indirect Neighbors through Graph Linear Transformations

  Marshall Rosenhoover, Huaming Zhang · 19 janvier 2026

  Graph neural networks (GNN) typically rely on localized message passing, requiring increasing depth to capture long range dependencies. In this work, we introduce Graph Linear Transformations, a linear transformation that realizes direct and indirect feature mixing on graphs through a single, well-d…

• Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge

  Runhao Zhao, Weixin Zeng, Wentao Zhang, Chong Chen, Zhengpin Li, Xiang Zhao, Lei Chen · 16 janvier 2026

  Domain-specific knowledge graphs (DKGs) often lack coverage compared to general knowledge graphs (GKGs). To address this, we introduce Domain-specific Knowledge Graph Fusion (DKGF), a novel task that enriches DKGs by integrating relevant facts from GKGs. DKGF faces two key challenges: high ambiguity…

• Knowledge Homophily in Large Language Models

  Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar, Nedim Lipka, Ryan A. Rossi, Franck Dernoncourt, Yu Zhang, Yao Ma, Yu Wang · 16 janvier 2026

  Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking. However, the structural organization of their knowledge remains unexplored. Inspired by cognitive neuroscience findings,…

• PLGC: Pseudo-Labeled Graph Condensation

  Jay Nandy, Arnab Kumar Mondal, Anuj Rathore, Mahesh Chandran · 16 janvier 2026

  Large graph datasets make training graph neural networks (GNNs) computationally costly. Graph condensation methods address this by generating small synthetic graphs that approximate the original data. However, existing approaches rely on clean, supervised labels, which limits their reliability when …

• MMPG: MoE-based Adaptive Multi-Perspective Graph Fusion for Protein Representation Learning

  Yusong Wang, Jialun Shen, Zhihao Wu, Yicheng Xu, Shiyin Tan, Mingkun Xu, Changshuo Wang, Zixing Song, Prayag Tiwari · 16 janvier 2026

  Graph Neural Networks (GNNs) have been widely adopted for Protein Representation Learning (PRL), as residue interaction networks can be naturally represented as graphs. Current GNN-based PRL methods typically rely on single-perspective graph construction strategies, which capture partial properties …

• Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering

  Yu Takahashi, Shun Takeuchi, Kexuan Xin, Guillaume Pelat, Yoshiaki Ikai, Junya Saito, Jonathan Vitale, Shlomo Berkovsky, Amin Beheshti · 16 janvier 2026

  Question answering (QA) systems are increasingly deployed across domains. However, their reliability is undermined when retrieved evidence is incomplete, noisy, or uncertain. Existing knowledge graph (KG) based QA frameworks typically represent facts as static and deterministic, failing to capture t…

• Attn-JGNN: Attention Enhanced Join-Graph Neural Networks

  Jixin Zhang · 16 janvier 2026

  We propose an Attention Enhanced Join-Graph Neural Networks(Attn-JGNN) model for solving #SAT problems, which significantly improves the solving accuracy. Inspired by the Iterative Join Graph Propagation (IJGP) algorithm, Attn-JGNN uses tree decomposition to encode the CNF formula into a join-graph,…

• SciNets: Graph-Constrained Multi-Hop Reasoning for Scientific Literature Synthesis

  Sauhard Dubey · 16 janvier 2026

  Cross-domain scientific synthesis requires connecting mechanistic explanations across fragmented literature, a capability that remains challenging for both retrieval-based systems and unconstrained language models. While recent work has applied large language models to scientific summarization and q…

• Simple Network Graph Comparative Learning

  Qiang Yu, Xinran Cheng, Shiqiang Xu, Chuanyi Liu · 16 janvier 2026

  The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application of these methods to node classification tasks still faces a number of challenges.…

• Graph Regularized PCA

  Antonio Briola, Marwin Schmidt, Fabio Caccioli, Carlos Ros Perez, James Singleton, Christian Michler, Tomaso Aste · 16 janvier 2026

  High-dimensional data often exhibit dependencies among variables that violate the isotropic-noise assumption under which principal component analysis (PCA) is optimal. For cases where the noise is not independent and identically distributed across features (i.e., the covariance is not spherical) we …

• Benchmarking Positional Encodings for GNNs and Graph Transformers

  Florian Gr\"otschla, Jiaqing Xie, Roger Wattenhofer · 15 janvier 2026

  Positional Encodings (PEs) are essential for injecting structural information into Graph Neural Networks (GNNs), particularly Graph Transformers, yet their empirical impact remains insufficiently understood. We introduce a unified benchmarking framework that decouples PEs from architectural choices,…

• FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks

  Renqiang Luo, Huafei Huang, Tao Tang, Jing Ren, Ziqi Xu, Mingliang Hou, Enyan Dai, Feng Xia · 15 janvier 2026

  Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. …

• Scalable and Reliable Evaluation of AI Knowledge Retrieval Systems: RIKER and the Coherent Simulated Universe

  JV Roig · 15 janvier 2026

  Evaluating knowledge systems (LLMs, RAG, knowledge graphs, etc) faces fundamental challenges: static benchmarks are vulnerable to contamination, LLM-based judges exhibit systematic biases, and ground truth extraction requires expensive human annotation. We present RIKER (Retrieval Intelligence and K…

• From Symbolic to Natural-Language Relations: Rethinking Knowledge Graph Construction in the Era of Large Language Models

  Kanyao Han, Yushang Lai · 15 janvier 2026

  Knowledge graphs (KGs) have commonly been constructed using predefined symbolic relation schemas, typically implemented as categorical relation labels. This design has notable shortcomings: real-world relations are often contextual, nuanced, and sometimes uncertain, and compressing it into discrete …

• Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning

  Shaotian Yan, Kaiyuan Liu, Chen Shen, Bing Wang, Sinan Fan, Jun Zhang, Yue Wu, Zheng Wang, Jieping Ye · 15 janvier 2026

  In this report, we introduce DASD-4B-Thinking, a lightweight yet highly capable, fully open-source reasoning model. It achieves SOTA performance among open-source models of comparable scale across challenging benchmarks in mathematics, scientific reasoning, and code generation -- even outperforming …

• FairGU: Fairness-aware Graph Unlearning in Social Network

  Renqiang Luo, Yongshuai Yang, Huafei Huang, Qing Qing, Mingliang Hou, Ziqi Xu, Yi Yu, Jingjing Zhou, Feng Xia · 15 janvier 2026

  Graph unlearning has emerged as a critical mechanism for supporting sustainable and privacy-preserving social networks, enabling models to remove the influence of deleted nodes and thereby better safeguard user information. However, we observe that existing graph unlearning techniques insufficiently…

• ReGraM: Region-First Knowledge Graph Reasoning for Medical Question Answering

  Chaerin Lee, Sohee Park, Hyunsik Na, Daseon Choi · 15 janvier 2026

  Recent studies in medical question answering (Medical QA) have actively explored the integration of large language models (LLMs) with biomedical knowledge graphs (KGs) to improve factual accuracy. However, most existing approaches still rely on traversing the entire KG or performing large-scale retr…

• Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection

  Tianyi Niu, Justin Chih-Yao Chen, Genta Indra Winata, Shi-Xiong Zhang, Supriyo Chakraborty, Sambit Sahu, Yue Zhang, Elias Stengel-Eskin, Mohit Bansal · 15 janvier 2026

  Large Language Model (LLM) routers dynamically select optimal models for given inputs. Existing approaches typically assume access to ground-truth labeled data, which is often unavailable in practice, especially when user request distributions are heterogeneous and unknown. We introduce Routing with…

• Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settings

  Georgios Chalkiadakis, Charilaos Akasiadis, Gerasimos Koresis, Stergios Plataniotis, Leonidas Bakopoulos · 15 janvier 2026

  This paper provides a comprehensive review of mainly GNN, DRL, and PTM methods with a focus on their potential incorporation in strategic multiagent settings. We draw interest in (i) ML methods currently utilized for uncovering unknown model structures adaptable to the task of strategic opponent mod…