Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Federated Learning with Graph-Based Aggregation for Traffic Forecasting
In traffic prediction, the goal is to estimate traffic speed or flow in specific regions or road segments using historical data collected by devices deployed in each area. Each region or road segment can be viewed as an …
Federated LearningGraph LearningTraffic PredictionSigned Graph Learning: Algorithms and Theory
Real-world data is often represented through the relationships between data samples, forming a graph structure. In many applications, it is necessary to learn this graph structure from the observed data. Current graph le…
Graph LearningGRIT: Graph Transformer For Internal Ice Layer Thickness Prediction
Gaining a deeper understanding of the thickness and variability of internal ice layers in Radar imagery is essential in monitoring the snow accumulation, better evaluating ice dynamics processes, and minimizing uncertain…
Graph LearningST-GRIT: Spatio-Temporal Graph Transformer For Internal Ice Layer Thickness Prediction
Understanding the thickness and variability of internal ice layers in radar imagery is crucial for monitoring snow accumulation, assessing ice dynamics, and reducing uncertainties in climate models. Radar sensors, capabl…
Graph LearningRethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity
As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an imp…
Anomaly DetectionGraph LearningGraph Learning
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 …
Drug DiscoveryFairnessFraud DetectionGraph Learning+2NTSFormer: A Self-Teaching Graph Transformer for Multimodal Isolated Cold-Start Node Classification
Isolated cold-start node classification on multimodal graphs is challenging because such nodes have no edges and often have missing modalities (e.g., absent text or image features). Existing methods address structural is…
Node ClassificationGraph LearningGDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
Dynamic Text-Attributed Graphs (DyTAGs), which intricately integrate structural, temporal, and textual attributes, are crucial for modeling complex real-world systems. However, most of the existing DyTAG datasets exhibit…
BenchmarkingGraph GenerationGraph LearningS2FGL: Spatial Spectral Federated Graph Learning
Federated Graph Learning (FGL) combines the privacy-preserving capabilities of federated learning (FL) with the strong graph modeling capability of Graph Neural Networks (GNNs). Current research addresses subgraph-FL onl…
Federated LearningGraph LearningPrivacy PreservingNonparametric learning of heterogeneous graphical model on network-linked data
Graphical models have been popularly used for capturing conditional independence structure in multivariate data, which are often built upon independent and identically distributed observations, limiting their applicabili…
Graph LearningA 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 LearningInterpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning
Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict concepts and then use them to perform a down…
Graph LearningSelf-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
We present LaplaceGNN, a novel self-supervised graph learning framework that bypasses the need for negative sampling by leveraging spectral bootstrapping techniques. Our method integrates Laplacian-based signals into the…
Graph LearningExploring Graph-Transformer Out-of-Distribution Generalization Abilities
Deep learning on graphs has shown remarkable success across numerous applications, including social networks, bio-physics, traffic networks, and recommendation systems. Regardless of their successes, current methods freq…
Domain GeneralizationGraph LearningOut-of-Distribution GeneralizationRecommendation SystemsAutomated Generation of Diverse Courses of Actions for Multi-Agent Operations using Binary Optimization and Graph Learning
Operations in disaster response, search \& rescue, and military missions that involve multiple agents demand automated processes to support the planning of the courses of action (COA). Moreover, traverse-affecting change…
Disaster ResponseDiversityGraph LearningGraph Neural NetworkFast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning
Equivariant Graph Neural Networks (GNNs) have achieved remarkable success across diverse scientific applications. However, existing approaches face critical efficiency challenges when scaling to large geometric graphs an…
Graph LearningHigher-Order Graph Databases
Recent advances in graph databases (GDBs) have been driving interest in large-scale analytics, yet current systems fail to support higher-order (HO) interactions beyond first-order (one-hop) relations, which are crucial …
Graph LearningSubgraph CountingAutomated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
Effective decision-making on networks often relies on learning from graph-structured data, where Graph Neural Networks (GNNs) play a central role, but they take efforts to configure and tune. In this demo, we propose LLM…
Decision MakingGraph LearningRAGRetrieval-augmented GenerationDynamic Graph Condensation
Recent research on deep graph learning has shifted from static to dynamic graphs, motivated by the evolving behaviors observed in complex real-world systems. However, the temporal extension in dynamic graphs poses signif…
Graph LearningDelving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification tasks but struggle with label noise in real-world data. Existing studies on graph learning with label noise commonly rely on …
BenchmarkingGraph LearningNode Classification