SOTAVerified

Graph Learning

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.

Papers

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Showing 476–500 of 1570 papers

TitleStatusHype
DE-HNN: An effective neural model for Circuit Netlist representationCode1
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
MAPL: Model Agnostic Peer-to-peer LearningCode0
Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting—0
Instruction-based Hypergraph Pretraining—0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Tensor-based Graph Learning with Consistency and Specificity for Multi-view ClusteringCode0
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph—0
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural NetworkCode0
CaseLink: Inductive Graph Learning for Legal Case RetrievalCode1
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions—0
Graphs Generalization under Distribution Shifts—0
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual ConnectionsCode1
Segment Anything Model for Road Network Graph ExtractionCode3
Mitigating Subpopulation Bias for Fair Network Topology Inference—0
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network—0
FairSTG: Countering performance heterogeneity via collaborative sample-level optimization—0
STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space ModelCode3
Molecular Classification Using Hyperdimensional Graph Classification—0
Graph Unitary Message Passing—0
Forward Learning of Graph Neural NetworksCode1
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs—0
Robust Subgraph Learning by Monitoring Early Training Representations—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified