Graph Learning
473 papers with code • 1 benchmarks • 8 datasets
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.
Benchmarks
These leaderboards are used to track progress in Graph Learning
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Libraries
Use these libraries to find Graph Learning models and implementationsDatasets
BotNet
Mindboggle
MIMIC-SPARQL
ETHZ-Shape
OCB
Latest papers with no code
CNN2GNN: How to Bridge CNN with GNN
Notably, due to extracting the intra-sample representation of a single instance and the topological relationship among the datasets simultaneously, the performance of distilled ``boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications
Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and reducing the cost of manual annotation.
Time-aware Heterogeneous Graph Transformer with Adaptive Attention Merging for Health Event Prediction
The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods.
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields.
SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling
We propose a novel Subgraph Pattern GNN (SPGNN) architecture that incorporates these enhancements.
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching
In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning layer.
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information.
Introducing Graph Learning over Polytopic Uncertain Graph
This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i. e., the graph is not exactly known, but its parameters or properties vary within a known range.
Characterizing the Influence of Topology on Graph Learning Tasks
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph.
