Advanced Graph Neural Networks

Derniers papiers

• Parameter Averaging in Link Prediction

  Rupesh Sapkota, Caglar Demir, Arnab Sharma, Axel-Cyrille Ngonga Ngomo · 30 octobre 2025

  Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and…

• A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring

  Michele Fontanesi, Alessio Micheli, Marco Podda, Domenico Tortorella · 30 octobre 2025

  Graph neural networks have become the de facto model for learning from structured data. However, the decision-making process of GNNs remains opaque to the end user, which undermines their use in safety-critical applications. Several explainable AI techniques for graphs have been developed to address…

• Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Training

  Aditya K. Ranjan, Siddharth Singh, Cunyang Wei, Abhinav Bhatele · 30 octobre 2025

  Graph neural networks (GNNs) leverage the connectivity and structure of real-world graphs to learn intricate properties and relationships between nodes. Many real-world graphs exceed the memory capacity of a GPU due to their sheer size, and training GNNs on such graphs requires techniques such as mi…

• WBT-BGRL: A Non-Contrastive Weighted Bipartite Link Prediction Model for Inductive Learning

  Joel Frank Huarayo Quispe, Lilian Berton, Didier Vega-Oliveros · 30 octobre 2025

  Link prediction in bipartite graphs is crucial for applications like recommendation systems and failure detection, yet it is less studied than in monopartite graphs. Contrastive methods struggle with inefficient and biased negative sampling, while non-contrastive approaches rely solely on positive s…

• Taming the Real-world Complexities in CPT E/M Coding with Large Language Models

  Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li · 30 octobre 2025

  Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians' best interest to provide accurate CPT E/M codes. %While important, it is an auxili…

• Transformers Provably Learn Directed Acyclic Graphs via Kernel-Guided Mutual Information

  Yuan Cheng, Yu Huang, Zhe Xiong, Yingbin Liang, Vincent Y. F. Tan · 30 octobre 2025

  Uncovering hidden graph structures underlying real-world data is a critical challenge with broad applications across scientific domains. Recently, transformer-based models leveraging the attention mechanism have demonstrated strong empirical success in capturing complex dependencies within graphs. H…

• Graph Mixing Additive Networks

  Maya Bechler-Speicher, Andrea Zerio, Maor Huri, Marie Vibeke Vestergaard, Ran Gilad-Bachrach, Tine Jess, Samir Bhatt, Aleksejs Sazonovs · 30 octobre 2025

  We introduce GMAN, a flexible, interpretable, and expressive framework that extends Graph Neural Additive Networks (GNANs) to learn from sets of sparse time-series data. GMAN represents each time-dependent trajectory as a directed graph and applies an enriched, more expressive GNAN to each graph. It…

• Bridging the Divide: End-to-End Sequence-Graph Learning

  Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk, Nam H. Nguyen, Furong Huang, C. Bayan Bruss, Rizal Fathony · 30 octobre 2025

  Many real-world datasets are both sequential and relational: each node carries an event sequence while edges encode interactions. Existing methods in sequence modeling and graph modeling often neglect one modality or the other. We argue that sequences and graphs are not separate problems but complem…

• Adaptive Frontier Exploration on Graphs with Applications to Network-Based Disease Testing

  Davin Choo, Yuqi Pan, Tonghan Wang, Milind Tambe, Alastair van Heerden, Cheryl Johnson · 30 octobre 2025

  We study a sequential decision-making problem on a $n$-node graph $\mathcal{G}$ where each node has an unknown label from a finite set $\mathbf{\Omega}$, drawn from a joint distribution $\mathcal{P}$ that is Markov with respect to $\mathcal{G}$. At each step, selecting a node reveals its label and y…

• The Underappreciated Power of Vision Models for Graph Structural Understanding

  Xinjian Zhao, Wei Pang, Zhongkai Xue, Xiangru Jian, Lei Zhang, Yaoyao Xu, Xiaozhuang Song, Shu Wu, Tianshu Yu · 30 octobre 2025

  Graph Neural Networks operate through bottom-up message-passing, fundamentally differing from human visual perception, which intuitively captures global structures first. We investigate the underappreciated potential of vision models for graph understanding, finding they achieve performance comparab…

• Learning Fair Graph Representations with Multi-view Information Bottleneck

  Chuxun Liu, Debo Cheng, Qingfeng Chen, Jiangzhang Gan, Jiuyong Li, Lin Liu · 30 octobre 2025

  Graph neural networks (GNNs) excel on relational data by passing messages over node features and structure, but they can amplify training data biases, propagating discriminatory attributes and structural imbalances into unfair outcomes. Many fairness methods treat bias as a single source, ignoring d…

• Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models

  Ben Finkelshtein, \.Ismail \.Ilkan Ceylan, Michael Bronstein, Ron Levie · 29 octobre 2025

  Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph machine learning: how to build graph foundation models capable of generalizing across arbitrary graphs and features? In …

• FoGE: Fock Space inspired encoding for graph prompting

  Sotirios Panagiotis Chytas, Rudrasis Chakraborty, Vikas Singh · 29 octobre 2025

  Recent results show that modern Large Language Models (LLM) are indeed capable of understanding and answering questions about structured data such as graphs. This new paradigm can lead to solutions that require less supervision while, at the same time, providing a model that can generalize and answe…

• The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics

  Marco S\"alzer, Przemys{\l}aw Andrzej Wa{\l}\k{e}ga, Martin Lange · 29 octobre 2025

  In recent years, the expressive power of various neural architectures -- including graph neural networks (GNNs), transformers, and recurrent neural networks -- has been characterised using tools from logic and formal language theory. As the capabilities of basic architectures are becoming well under…

• GraphNet: A Large-Scale Computational Graph Dataset for Tensor Compiler Research

  Xinqi Li, Yiqun Liu, Shan Jiang, Enrong Zheng, Huaijin Zheng, Wenhao Dai, Haodong Deng, Dianhai Yu, Yanjun Ma · 29 octobre 2025

  We introduce GraphNet, a dataset of 2.7K real-world deep learning computational graphs with rich metadata, spanning six major task categories across multiple deep learning frameworks. To evaluate tensor compiler performance on these samples, we propose the benchmark metric Speedup Score S(t), which …

• Temporal Knowledge Graph Hyperedge Forecasting: Exploring Entity-to-Category Link Prediction

  Edward Markai, Sina Molavipour · 29 octobre 2025

  Temporal Knowledge Graphs have emerged as a powerful way of not only modeling static relationships between entities but also the dynamics of how relations evolve over time. As these informational structures can be used to store information from a real-world setting, such as a news flow, predicting f…

• Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks

  Faezeh Dehghan Tarzjani, Bhaskar Krishnamachari · 29 octobre 2025

  The p-persistent CSMA protocol is central to random-access MAC analysis, but predicting saturation throughput in heterogeneous multi-hop wireless networks remains a hard problem. Simplified models that assume a single, shared interference domain can underestimate throughput by 48-62% in sparse topol…

• QoSGMAA: A Robust Multi-Order Graph Attention and Adversarial Framework for Sparse QoS Prediction

  Guanchen Du, Jianlong Xu, Mingtong Li, Ruiqi Wang, Qianqing Guo, Caiyi Chen, Qingcao Dai, Yuxiang Zeng · 28 octobre 2025

  With the rapid advancement of internet technologies, network services have become critical for delivering diverse and reliable applications to users. However, the exponential growth in the number of available services has resulted in many similar offerings, posing significant challenges in selecting…

• Learning from Frustration: Torsor CNNs on Graphs

  Daiyuan Li, Shreya Arya, Robert Ghrist · 28 octobre 2025

  Most equivariant neural networks rely on a single global symmetry, limiting their use in domains where symmetries are instead local. We introduce Torsor CNNs, a framework for learning on graphs with local symmetries encoded as edge potentials-- group-valued transformations between neighboring coordi…

• Adaptive Dual Prompting: Hierarchical Debiasing for Fairness-aware Graph Neural Networks

  Yuhan Yang, Xingbo Fu, Jundong Li · 28 octobre 2025

  In recent years, pre-training Graph Neural Networks (GNNs) through self-supervised learning on unlabeled graph data has emerged as a widely adopted paradigm in graph learning. Although the paradigm is effective for pre-training powerful GNN models, the objective gap often exists between pre-training…

• Prefetching Cache Optimization Using Graph Neural Networks: A Modular Framework and Conceptual Analysis

  F. I. Qowy · 28 octobre 2025

  Caching and prefetching techniques are fundamental to modern computing, serving to bridge the growing performance gap between processors and memory. Traditional prefetching strategies are often limited by their reliance on predefined heuristics or simplified statistical models, which fail to capture…

• Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers

  Dongyi Liu, Jiangtong Li, Dawei Cheng, Changjun Jiang · 28 octobre 2025

  Graph Neural Networks(GNNs) are vulnerable to backdoor attacks, where adversaries implant malicious triggers to manipulate model predictions. Existing trigger generators are often simplistic in structure and overly reliant on specific features, confining them to a single graph learning paradigm, s…

• Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning

  Torben Berndt, Benjamin Walker, Tiexin Qin, Jan St\"uhmer, Andrey Kormilitzin · 28 octobre 2025

  Dynamic graphs exhibit complex temporal dynamics due to the interplay between evolving node features and changing network structures. Recently, Graph Neural Controlled Differential Equations (Graph Neural CDEs) successfully adapted Neural CDEs from paths on Euclidean domains to paths on graph domain…

• Transfer Learning on Edge Connecting Probability Estimation under Graphon Model

  Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng · 28 octobre 2025

  Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practic…

• Enhancing Graph Neural Networks: A Mutual Learning Approach

  Paul Agbaje, Arkajyoti Mitra, Afia Anjum, Pranali Khose, Ebelechukwu Nwafor, Habeeb Olufowobi · 28 octobre 2025

  Knowledge distillation (KD) techniques have emerged as a powerful tool for transferring expertise from complex teacher models to lightweight student models, particularly beneficial for deploying high-performance models in resource-constrained devices. This approach has been successfully applied to g…