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 776–800 of 1570 papers

TitleStatusHype
Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
Heterogeneous Graph Neural Network via Attribute Completion—0
Decoupling feature propagation from the design of graph auto-encoders—0
Heterogeneous Graph Sparsification for Efficient Representation Learning—0
Structured Graph Learning for Clustering and Semi-supervised Classification—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion—0
Deconvolutional Networks on Graph Data—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach—0
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting—0
Hierarchical Transformer for Scalable Graph Learning—0
Decomposing User-APP Graph into Subgraphs for Effective APP and User Embedding Learning—0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning—0
Dataset Condensation with Latent Quantile Matching—0
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view—0
Higher-order Structure Based Anomaly Detection on Attributed Networks—0
Higher-order Structure Boosts Link Prediction on Temporal Graphs—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data—0
Hippocampal Spatial Mapping As Fast Graph Learning—0
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting—0
A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified