Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Multivariate time series forecasting (MTSF) often faces challenges from missing variables, which hinder conventional spatial-temporal graph neural networks in modeling inter-variable correlations. While GinAR addresses v…
Multivariate Time Series ForecastingGraph LearningBringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Graph foundation models (GFMs) have recently emerged as a promising paradigm for achieving broad generalization across various graph data. However, existing GFMs are often trained on datasets that may not fully reflect r…
Time Series ForecastingNode ClassificationGraph LearningMCIGLE: Multimodal Exemplar-Free Class-Incremental Graph Learning
Exemplar-free class-incremental learning enables models to learn new classes over time without storing data from old ones. As multimodal graph-structured data becomes increasingly prevalent, existing methods struggle wit…
class-incremental learningGraph LearningMillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
Multi-variate time series (MTS) forecasting is crucial for various applications. Existing methods have shown promising results owing to their strong ability to capture intra- and inter-variate dependencies. However, thes…
Time Series ForecastingGraph LearningRankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation
Cross-domain recommendation systems face the challenge of integrating fine-grained user and item relationships across various product domains. To address this, we introduce RankGraph, a scalable graph learning framework …
Recommendation SystemsGraph Neural NetworkContrastive LearningGraph LearningTransformer-Guided Content-Adaptive Graph Learning for Hyperspectral Unmixing
Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundances. Despite significant progress in this field using deep learning, most…
Graph Neural NetworkGraph LearningACA-Net: Future Graph Learning for Logistical Demand-Supply Forecasting
Logistical demand-supply forecasting that evaluates the alignment between projected supply and anticipated demand, is essential for the efficiency and quality of on-demand food delivery platforms and serves as a key indi…
Graph LearningADMP-GNN: Adaptive Depth Message Passing GNN
Graph Neural Networks (GNNs) have proven to be highly effective in various graph learning tasks. A key characteristic of GNNs is their use of a fixed number of message-passing steps for all nodes in the graph, regardless…
Node ClassificationGraph LearningTask-Aware Adaptive Modulation: A Replay-Free and Resource-Efficient Approach For Continual Graph Learning
Continual Graph Learning(CGL)focuses on acquiring new knowledge while retaining previously learned information, essential for real-world graph applications. Current methods grapple with two main issues:1) The Stability-P…
Graph LearningFlow Matters: Directional and Expressive GNNs for Heterophilic Graphs
In heterophilic graphs, where neighboring nodes often belong to different classes, conventional Graph Neural Networks (GNNs) struggle due to their reliance on local homophilous neighborhoods. Prior studies suggest that m…
Node ClassificationGraph LearningGSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning
Self-supervised learning (SSL) has shown great promise in graph representation learning. However, most existing graph SSL methods are developed and evaluated under a single-dataset setting, leaving their cross-dataset tr…
Graph Representation LearningSelf-Supervised LearningGraph LearningBeyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
Estimating the quality of register transfer level (RTL) designs is crucial in the electronic design automation (EDA) workflow, as it enables instant feedback on key performance metrics like area and delay without the nee…
Self-Supervised LearningRepresentation LearningKnowledge DistillationGraph LearningQuantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning
We propose the Quantum Graph Attention Network (QGAT), a hybrid graph neural network that integrates variational quantum circuits into the attention mechanism. At its core, QGAT employs strongly entangling quantum circui…
Graph Neural NetworkGraph LearningLatent Graph Learning in Generative Models of Neural Signals
Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience. Foundation models for large-scale neural data represent shared…
Graph LearningAn Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problem
Hybridizing machine learning techniques with metaheuristics has attracted significant attention in recent years. Many attempts employ supervised or reinforcement learning to support the decision-making of heuristic metho…
Graph Representation LearningReinforcement LearningGraph LearningGraph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: ef…
Graph structure learningRepresentation LearningGraph LearningAGP: A Novel Arabidopsis thaliana Genomics-Phenomics Dataset and its HyperGraph Baseline Benchmarking
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 LearningA Remedy for Over-Squashing in Graph Learning via Forman-Ricci Curvature based Graph-to-Hypergraph Structural Lifting
Graph Neural Networks are highly effective at learning from relational data, leveraging node and edge features while maintaining the symmetries inherent to graph structures. However, many real-world systems, such as soci…
Graph LearningDFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning
Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant cha…
Federated LearningGraph LearningGraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generation
Federated graph learning (FGL) enables multiple clients to collaboratively train powerful graph neural networks without sharing their private, decentralized graph data. Inherited from generic federated learning, FGL is c…
Federated LearningData AugmentationGraph Learning