Papers Graph Regression
“Graph Regression” 태그가 달린 논문 155편 · 필터 해제
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…
Graph Representation LearningSelf-Supervised LearningGraph ClassificationGraph RegressionGRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana
Understanding which genes control which traits in an organism remains one of the central challenges in biology. Despite significant advances in data collection technology, our ability to map genes to traits is still limi…
Graph RegressionGraph LearningGeodesics of Dynamic Graphs for Regime Change Detection
Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-world applications, such as social netwo…
Change Point DetectionChange DetectionGraph RegressionWeisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
Graphs with a simple spectrum admit cubic-time isomorphism testing, yet we prove that for every natural number $k$, the $k$-Weisfeiler-Leman ($k$-WL) test cannot distinguish all non-isomorphic graphs with a simple spectr…
Graph RegressionBOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with generalization. We introduce BOOST-RPF, a n…
Graph RegressionMolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks
Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction. Research has demonstrated that SMILES can be converted…
Molecular Property PredictionTransfer LearningGraph RegressionStuart-Landau Oscillatory Graph Neural Network
Oscillatory Graph Neural Networks (OGNNs) are an emerging class of physics-inspired architectures designed to mitigate oversmoothing and vanishing gradient problems in deep GNNs. In this work, we introduce the Complex-Va…
Graph ClassificationGraph Neural NetworkNode ClassificationGraph RegressionUniGTE: Unified Graph-Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture gr…
Zero-shot GeneralizationGraph ClassificationNode ClassificationGraph RegressionJaGuard: Position Error Correction of GNSS Jamming with Deep Temporal Graphs
Global Navigation Satellite Systems (GNSS) face growing disruption from intentional jamming, undermining critical infrastructure where precise positioning and timing are essential. Current position error correction (PEC)…
Graph RegressionA Recipe for Causal Graph Regression: Confounding Effects Revisited
Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios. However, the e…
Contrastive LearningGraph RegressionGraph LearningGraph Neural Networks for Jamming Source Localization
Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored. In this work, we introdu…
feature selectiongraph constructionGraph Neural NetworkGraph RegressionImproving the Effective Receptive Field of Message-Passing Neural Networks
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena such as over-squashing, where long-range d…
Graph ClassificationGraph RegressionNode ClassificationA Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants
Graph-level regression underpins many real-world applications, yet public benchmarks remain heavily skewed toward molecular graphs and citation networks. This limited diversity hinders progress on models that must genera…
Graph Neural NetworkGraph RegressionregressionGotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships an…
Atomic ForcesComputational EfficiencyGraph Property PredictionGraph Regression+1Power Spectrum Signatures of Graphs
Point signatures based on the Laplacian operators on graphs, point clouds, and manifolds have become popular tools in machine learning for graphs, clustering, and shape analysis. In this work, we propose a novel point si…
DescriptiveGraph RegressionPre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled…
Graph ClassificationGraph Neural NetworkGraph RegressionMolecular Property Prediction+1Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies, while Graph Transfor…
Graph ClassificationGraph Property PredictionGraph RegressionNode ClassificationLearning Efficient Positional Encodings with Graph Neural Networks
Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectures and increase the expressive capacity …
Graph RegressionGraph Representation LearningPositionRepresentation LearningBeyond Message Passing: Neural Graph Pattern Machine
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downstream performance. However, most existin…
Graph ClassificationGraph LearningGraph RegressionLink Prediction+2Molecular Fingerprints Are Strong Models for Peptide Function Prediction
We study the effectiveness of molecular fingerprints for peptide property prediction and demonstrate that domain-specific feature extraction from molecular graphs can outperform complex and computationally expensive mode…
Graph ClassificationGraph RegressionProperty Prediction