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 1001–1025 of 1570 papers

TitleStatusHype
TF-GNN: Graph Neural Networks in TensorFlowCode3
Pure Transformers are Powerful Graph LearnersCode1
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
Graph Learning based Generative Design for Resilience of Interdependent Network Systems—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning—0
Primitive Graph Learning for Unified Vector Mapping—0
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning—0
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic ForecastingCode2
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
All the World's a (Hyper)Graph: A Data DramaCode1
On the Surprising Behaviour of node2vecCode0
Long Range Graph BenchmarkCode1
NCAGC: A Neighborhood Contrast Framework for Attributed Graph ClusteringCode1
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation—0
Universally Expressive Communication in Multi-Agent Reinforcement LearningCode0
Semi-Supervised Hierarchical Graph Classification—0
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large GraphsCode1
GRETEL: A unified framework for Graph Counterfactual Explanation EvaluationCode1
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Approximate Network Motif Mining Via Graph LearningCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Negative Sampling for Contrastive Representation Learning: A Review—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified