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 901–925 of 1570 papers

TitleStatusHype
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
Open-World Lifelong Graph LearningCode0
Towards Graph Foundation Models: A Survey and Beyond—0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation—0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Infinite Width Graph Neural Networks for Node Regression/ ClassificationCode0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Supercharging Graph Transformers with Advective Diffusion—0
Integrating Graphs with Large Language Models: Methods and Prospects—0
Data-centric Graph Learning: A Survey—0
Graph learning in robotics: a survey—0
HoloNets: Spectral Convolutions do extend to Directed GraphsCode0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks—0
Deep Insights into Noisy Pseudo Labeling on Graph DataCode0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—0
ResolvNet: A Graph Convolutional Network with multi-scale Consistency—0
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit FieldsCode0
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction—0
Neuromorphic Imaging and Classification with Graph Learning—0
ALEX: Towards Effective Graph Transfer Learning with Noisy Labels—0
TouchUp-G: Improving Feature Representation through Graph-Centric FinetuningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified