Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized…
Node ClassificationGraph LearningUnlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting
Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stakeholders poses significant challenges in …
Federated LearningGraph LearningGeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs
Graph neural networks (GNNs) on text--attributed graphs (TAGs) typically encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear neighborhood aggregation. However, the repr…
Graph LearningOne Router to Route Them All: Homogeneous Expert Routing for Heterogeneous Graph Transformers
A common practice in heterogeneous graph neural networks (HGNNs) is to condition parameters on node/edge types, assuming types reflect semantic roles. However, this can cause overreliance on surface-level labels and impe…
Link PredictionGraph LearningAdaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interconnected reservoirs. We introduce AdaTrip…
Graph LearningLaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation
Centerline graphs, crucial for path planning in autonomous driving, are traditionally learned using deterministic methods. However, these methods often lack spatial reasoning and struggle with occluded or invisible cente…
Autonomous DrivingSpatial ReasoningGraph LearningSketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
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 …
Graph LearningEvolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments
Graph neural networks have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs. However, existing GNNs exhibit poor generalization ability under distribution shifts, which is inevit…
Graph GenerationGraph LearningGraph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Them
A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing approaches ignore features in favor of struc…
Graph LearningWindMiL: Equivariant Graph Learning for Wind Loading Prediction
Accurate prediction of wind loading on buildings is crucial for structural safety and sustainable design, yet conventional approaches such as wind tunnel testing and large-eddy simulation (LES) are prohibitively expensiv…
Graph LearningSpectral Neural Graph Sparsification
Graphs are central to modeling complex systems in domains such as social networks, molecular chemistry, and neuroscience. While Graph Neural Networks, particularly Graph Convolutional Networks, have become standard tools…
Graph Representation LearningCommunity DetectionGraph LearningRobust Graph Condensation via Classification Complexity Mitigation
Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph i…
Graph LearningAeolus: A Multi-structural Flight Delay Dataset
We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this dom…
Relational ReasoningGraph LearningHEIR: Learning Graph-Based Motion Hierarchies
Hierarchical structures of motion exist across research fields, including computer vision, graphics, and robotics, where complex dynamics typically arise from coordinated interactions among simpler motion components. Exi…
Graph LearningBridging the Divide: End-to-End Sequence-Graph Learning
Many real-world prediction tasks, particularly those involving entities such as customers or patients, involve both {sequential} and {relational} data. Each entity maintains its own sequence of events while simultaneousl…
Fraud DetectionGraph LearningGMTRouter: Personalized LLM Router over Multi-turn User Interactions
Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit diverse preferences, personalization has attracted increasing attention in LLM…
Graph LearningHyperGraphX: Graph Transductive Learning with Hyperdimensional Computing and Message Passing
We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For prediction accuracy \hdgc outperforms major and …
Graph Neural NetworkGraph LearningNeuroPathNet: Dynamic Path Trajectory Learning for Brain Functional Connectivity Analysis
Understanding the evolution of brain functional networks over time is of great significance for the analysis of cognitive mechanisms and the diagnosis of neurological diseases. Existing methods often have difficulty in c…
Trajectory ModelingGraph LearningToward Robust Signed Graph Learning through Joint Input-Target Denoising
Signed Graph Neural Networks (SGNNs) are widely adopted to analyze complex patterns in signed graphs with both positive and negative links. Given the noisy nature of real-world connections, the robustness of SGNN has als…
Graph Representation LearningGraph structure learningInformation ExtractionGraph LearningRandom Search Neural Networks for Efficient and Expressive Graph Learning
Random walk neural networks (RWNNs) have emerged as a promising approach for graph representation learning, leveraging recent advances in sequence models to process random walks. However, under realistic sampling constra…
Graph Representation LearningGraph Learning