Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emerge on unlabeled testing nodes. However, …
Graph LearningGraph Neural NetworkNode ClassificationNovel Class DiscoveryExploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting
In the evolving field of psychopathology, the accurate assessment and forecasting of data derived from Ecological Momentary Assessment (EMA) is crucial. EMA offers contextually-rich psychopathological measurements over t…
Graph LearningTime SeriesInferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph learning methods in MARL are limited. The…
Graph LearningMulti-agent Reinforcement LearningStarcraftStarcraft IIInstruction-based Hypergraph Pretraining
Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or classification. However, the gap betwee…
DiversityGraph LearningLink PredictionRepresentation LearningMAPL: Model Agnostic Peer-to-peer Learning
Effective collaboration among heterogeneous clients in a decentralized setting is a rather unexplored avenue in the literature. To structurally address this, we introduce Model Agnostic Peer-to-peer Learning (coined as M…
Graph LearningmodelPrivacy PreservingTensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering
In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consens…
ClusteringGraph LearningSpecificityLearn from Heterophily: Heterophilous Information-enhanced Graph Neural Network
Under circumstances of heterophily, where nodes with different labels tend to be connected based on semantic meanings, Graph Neural Networks (GNNs) often exhibit suboptimal performance. Current studies on graph heterophi…
Graph LearningGraph Neural NetworkNode ClassificationCaseLink: Inductive Graph Learning for Legal Case Retrieval
In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference r…
Graph LearningRetrievalSelf-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. However, classic non-hierarchical MARL algorithms still cannot address various complex multi-agent problems that requi…
ClusteringGraph LearningMulti-agent Reinforcement LearningGraphs Generalization under Distribution Shifts
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the training distribution. To address this …
AttributeGraph LearningCADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potent…
DecoderDrug–drug Interaction ExtractionGraph LearningEnhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks
Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spikin…
Graph LearningGraph Representation LearningRepresentation LearningSegment Anything Model for Road Network Graph Extraction
We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry, we formulate it as a dense semantic seg…
Graph LearningGraph Neural NetworkSemantic SegmentationVCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Graph transformer has been proven as an effective graph learning method for its adoption of attention mechanism that is capable of capturing expressive representations from complex topological and feature information of …
Feature EngineeringGraph LearningMitigating Subpopulation Bias for Fair Network Topology Inference
We consider fair network topology inference from nodal observations. Real-world networks often exhibit biased connections based on sensitive nodal attributes. Hence, different subpopulations of nodes may not share or rec…
Graph LearningUnifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations. The primary overhead of GNN update stems from graph propagation and weigh…
Graph LearningGraph Neural NetworkSTG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-stationary, leading to the continuous challenge of spatial-temporal graph learning. In the past few years, various GNN-based methods ha…
Computational EfficiencyGraph LearningMambaState Space ModelsFairSTG: Countering performance heterogeneity via collaborative sample-level optimization
Spatiotemporal learning plays a crucial role in mobile computing techniques to empower smart cites. While existing research has made great efforts to achieve accurate predictions on the overall dataset, they still neglec…
FairnessGraph LearningTransfer LearningMolecular Classification Using Hyperdimensional Graph Classification
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained sign…
ClassificationGraph ClassificationGraph LearningGraph Unitary Message Passing
Message passing mechanism contributes to the success of GNNs in various applications, but also brings the oversquashing problem. Recent works combat oversquashing by improving the graph spectrums with rewiring techniques…
Graph Learning