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 1151–1175 of 1570 papers

TitleStatusHype
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
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-LearningCode0
On the Surprising Behaviour of node2vecCode0
Universally Expressive Communication in Multi-Agent Reinforcement LearningCode0
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation—0
Semi-Supervised Hierarchical Graph Classification—0
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning—0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification—0
Negative Sampling for Contrastive Representation Learning: A Review—0
Spectral Maps for Learning on Subgraphs—0
Temporal Multiresolution Graph Neural Networks For Epidemic PredictionCode0
Deep Ensembles for Graphs with Higher-order DependenciesCode0
RecipeRec: A Heterogeneous Graph Learning Model for Recipe RecommendationCode0
Graph-Based Methods for Discrete ChoiceCode0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Weisfeiler and Leman Go Walking: Random Walk Kernels RevisitedCode0
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection—0
Learning Graph Structure from Convolutional Mixtures—0
Simplifying Node Classification on Heterophilous Graphs with Compatible Label PropagationCode0
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data—0
Trustworthy Graph Neural Networks: Aspects, Methods and Trends—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified