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 701–725 of 1570 papers

TitleStatusHype
Graph Edge Representation via Tensor Product Graph Convolutional Representation—0
A Scalable and Effective Alternative to Graph Transformers—0
A Comprehensive Survey of Foundation Models in Medicine—0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning—0
Dataset Condensation with Latent Quantile Matching—0
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure—0
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening—0
Scalable and Flexible Causal Discovery with an Efficient Test for AdjacencyCode0
Self-supervised Graph Neural Network for Mechanical CAD Retrieval—0
Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks—0
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation—0
On the Hölder Stability of Multiset and Graph Neural Networks—0
GKAN: Graph Kolmogorov-Arnold Networks—0
Async Learned User Embeddings for Ads Delivery Optimization—0
Higher-order Structure Based Anomaly Detection on Attributed Networks—0
PANDORA: Deep graph learning based COVID-19 infection risk level forecasting—0
Graph Mining under Data scarcity—0
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks—0
From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning—0
Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors—0
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal RepresentationCode0
Higher Order Structures For Graph Explanations—0
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding—0
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment—0
Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified