Graph Learning
471 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
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.
Spectral GNN via Two-dimensional (2-D) Graph Convolution
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph learning.
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
In this paper, we provide a systematic study of the theoretical expressive power of order-$k$ graph transformers and sparse variants.
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals.
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
However, the cross-item relations have been under-explored in the current multimodal pre-train models.
Continual Learning for Smart City: A Survey
We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.
