Advanced Graph Neural Networks

Derniers papiers

• Graph Learning

  Feng Xia, Ciyuan Peng, Jing Ren, Falih Gozi Febrinanto, Renqiang Luo, Vidya Saikrishna, Shuo Yu, Xiangjie Kong · 10 novembre 2025

  Graph learning has rapidly evolved into a critical subfield of machine learning and artificial intelligence (AI). Its development began with early graph-theoretic methods, gaining significant momentum with the advent of graph neural networks (GNNs). Over the past decade, progress in scalable archite…

• SPECTRA: Spectral Target-Aware Graph Augmentation for Imbalanced Molecular Property Regression

  Brenda Nogueira, Meng Jiang, Nitesh V. Chawla, Nuno Moniz · 10 novembre 2025

  In molecular property prediction, the most valuable compounds (e.g., high potency) often occupy sparse regions of the target space. Standard Graph Neural Networks (GNNs) commonly optimize for the average error, underperforming on these uncommon but critical cases, with existing oversampling methods …

• Building Specialized Software-Assistant ChatBot with Graph-Based Retrieval-Augmented Generation

  Mohammed Hilel, Yannis Karmim, Jean De Bodinat, Reda Sarehane, Antoine Gillon · 10 novembre 2025

  Digital Adoption Platforms (DAPs) have become essential tools for helping employees navigate complex enterprise software such as CRM, ERP, or HRMS systems. Companies like LemonLearning have shown how digital guidance can reduce training costs and accelerate onboarding. However, building and maintain…

• Neural Attention: A Novel Mechanism for Enhanced Expressive Power in Transformer Models

  Andrew DiGiugno, Ausif Mahmood · 10 novembre 2025

  Transformer models typically calculate attention matrices using dot products, which have limitations when capturing nonlinear relationships between embedding vectors. We propose Neural Attention, a technique that replaces dot products with feed-forward networks, enabling a more expressive representa…

• Learning Latent Graph Geometry via Fixed-Point Schr\"odinger-Type Activation: A Theoretical Study

  Dmitry Pasechnyuk-Vilensky, Martin Tak\'a\v{c} · 10 novembre 2025

  We develop a unified theoretical framework for neural architectures whose internal representations evolve as stationary states of dissipative Schr\"odinger-type dynamics on learned latent graphs. Each layer is defined by a fixed-point Schr\"odinger-type equation depending on a weighted Laplacian enc…

• Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training

  Yufei He, Zhenyu Hou, Yukuo Cen, Jun Hu, Feng He, Xu Cheng, Jie Tang, Bryan Hooi · 7 novembre 2025

  Graph pre-training has been concentrated on graph-level tasks involving small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre-trained models to web-scale graphs with billions of nodes in industrial scenarios, while avoiding negative transfer acr…

• Deep Graph Learning for Industrial Carbon Emission Analysis and Policy Impact

  Xuanming Zhang · 7 novembre 2025

  Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO…

• Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs

  Alberto Cattaneo, Carlo Luschi, Daniel Justus · 7 novembre 2025

  Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various solutions have been proposed, a comparison of methods is difficult due to the lack of challenging QA datasets with ground-truth targets for graph retrie…

• DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection

  Qingfeng Chen, Haojin Zeng, Jingyi Jie, Shichao Zhang, Debo Cheng · 7 novembre 2025

  With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graphs that deviate from normal behavioral patterns. However, most Graph Neural Network (GNN) approaches implicitly assume th…

• Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning

  Divyansha Lachi, Mahmoud Mohammadi, Joe Meyer, Vinam Arora, Tom Palczewski, Eva L. Dyer · 7 novembre 2025

  In domains such as healthcare, finance, and e-commerce, the temporal dynamics of relational data emerge from complex interactions-such as those between patients and providers, or users and products across diverse categories. To be broadly useful, models operating on these data must integrate long-ra…

• GRAD: Graph-Retrieved Adaptive Decoding for Hallucination Mitigation

  Manh Nguyen, Sunil Gupta, Dai Do, Hung Le · 7 novembre 2025

  Hallucination mitigation remains a persistent challenge for large language models (LLMs), even as model scales grow. Existing approaches often rely on external knowledge sources, such as structured databases or knowledge graphs, accessed through prompting or retrieval. However, prompt-based groundin…

• Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Models

  Yuxiao Wang, Di Wu, Feng Liu, Zhimin Qiu, Chenrui Hu · 7 novembre 2025

  This paper proposes a composable fine-tuning method that integrates graph structural priors with modular adapters to address the high computational cost and structural instability faced by large-scale pre-trained models in multi-task adaptation. The method introduces a relation matrix to model depen…

• Local Fragments, Global Gains: Subgraph Counting using Graph Neural Networks

  Shubhajit Roy, Shrutimoy Das, Binita Maity, Anant Kumar, Anirban Dasgupta · 7 novembre 2025

  Subgraph counting is a fundamental task for analyzing structural patterns in graph-structured data, with important applications in domains such as computational biology and social network analysis, where recurring motifs reveal functional and organizational properties. In this paper, we propose loca…

• Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks

  Ryien Hosseini, Filippo Simini, Venkatram Vishwanath, Rebecca Willett, Henry Hoffmann · 7 novembre 2025

  Graph Neural Networks learn on graph-structured data by iteratively aggregating local neighborhood information. While this local message passing paradigm imparts a powerful inductive bias and exploits graph sparsity, it also yields three key challenges: (i) oversquashing of long-range information, (…

• Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs

  Haolin Li, Haoyu Wang, Luana Ruiz · 6 novembre 2025

  Graph Neural Networks (GNNs) excel in many graph machine learning tasks but face challenges when scaling to large networks. GNN transferability allows training on smaller graphs and applying the model to larger ones, but existing methods often rely on random subsampling, leading to disconnected subg…

• Meta-Semantics Augmented Few-Shot Relational Learning

  Han Wu, Jie Yin · 6 novembre 2025

  Few-shot relational learning on knowledge graph (KGs) aims to perform reasoning over relations with only a few training examples. While current methods have focused primarily on leveraging specific relational information, rich semantics inherent in KGs have been largely overlooked. To bridge this ga…

• Discrete Bayesian Sample Inference for Graph Generation

  Ole Petersen, Marcel Kollovieh, Marten Lienen, Stephan G\"unnemann · 6 novembre 2025

  Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their discrete, unordered nature makes them difficult for traditional generative models, leading to the rise of discrete diffusion and flow matching models. In t…

• Homomorphism distortion: A metric to distinguish them all and in the latent space bind them

  Martin Carrasco, Olga Zaghen, Erik Bekkers, Bastian Rieck · 6 novembre 2025

  For far too long, expressivity of graph neural networks has been measured \emph{only} in terms of combinatorial properties. In this work we stray away from this tradition and provide a principled way to measure similarity between vertex attributed graphs. We denote this measure as the \emph{graph ho…

• GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Models

  Zhibin Wang, Zhixing Zhang, Shuqi Wang, Xuanting Xie, Zhao Kang · 6 novembre 2025

  Graph Neural Networks (GNNs) have demonstrated impressive performance on task-specific benchmarks, yet their ability to generalize across diverse domains and tasks remains limited. Existing approaches often struggle with negative transfer, scalability issues, and high adaptation costs. To address th…

• A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges

  Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Philip S. Yu, Ming Zhang · 6 novembre 2025

  Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. Significant strides have been made in leveraging Graph Neural Networks (GNNs) to achieve remarkable success…

• Causal Graph Neural Networks for Healthcare

  Munib Mesinovic, Max Buhlan, Tingting Zhu · 5 novembre 2025

  Healthcare artificial intelligence systems routinely fail when deployed across institutions, with documented performance drops and perpetuation of discriminatory patterns embedded in historical data. This brittleness stems, in part, from learning statistical associations rather than causal mechanism…

• PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks

  Fuyi Wang, Zekai Chen, Mingyuan Fan, Jianying Zhou, Lei Pan, Leo Yu Zhang · 5 novembre 2025

  Graph neural networks (GNNs) are powerful tools for analyzing and learning from graph-structured (GS) data, facilitating a wide range of services. Deploying such services in privacy-critical cloud environments necessitates the development of secure inference (SI) protocols that safeguard sensitive G…

• Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation

  Wongyu Kim, Hochang Lee, Sanghak Lee, Yoonsung Kim, Jaehyun Park · 5 novembre 2025

  Query augmentation makes queries more meaningful by appending further information to the queries to find relevant documents. Current studies have proposed Large Language Model (LLM)-based embedders, which learn representation for embedding and generation for query augmentation in a multi-task manner…

• Explainable Graph Neural Architecture Search via Monte-Carlo Tree Search (Full version)

  Yuya Sasaki · 5 novembre 2025

  The number of graph neural network (GNN) architectures has increased rapidly due to the growing adoption of graph analysis. Although we use GNNs in wide application scenarios, it is a laborious task to design/select optimal GNN architectures in diverse graphs. To reduce human efforts, graph neural a…

• UFGraphFR: Graph Federation Recommendation System based on User Text description features

  Xudong Wang, Qingbo Hao, Xu Cheng, Yingyuan Xiao · 5 novembre 2025

  Federated learning offers a privacy-preserving framework for recommendation systems by enabling local data processing; however, data localization introduces substantial obstacles. Traditional federated recommendation approaches treat each user as an isolated entity, failing to construct global user …