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 651–700 of 1570 papers

TitleStatusHype
Towards Graph Foundation Models: A Survey and Beyond—0
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation—0
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node RepresentationCode1
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
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
Multimodal Graph Learning for Generative TasksCode1
Supercharging Graph Transformers with Advective Diffusion—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
GraphLLM: Boosting Graph Reasoning Ability of Large Language ModelCode1
Integrating Graphs with Large Language Models: Methods and Prospects—0
Tailoring Self-Attention for Graph via Rooted SubtreesCode1
Data-centric Graph Learning: A Survey—0
Graph learning in robotics: a survey—0
HoloNets: Spectral Convolutions do extend to Directed GraphsCode0
Deep Insights into Noisy Pseudo Labeling on Graph DataCode0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks—0
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
One for All: Towards Training One Graph Model for All Classification TasksCode2
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit FieldsCode0
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
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
GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy ChangesCode0
TMac: Temporal Multi-Modal Graph Learning for Acoustic Event ClassificationCode1
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?—0
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis—0
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
A Versatile Graph Learning Approach through LLM-based Agent—0
Human Learning of Hierarchical Graphs—0
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning—0
Visual-Kinematics Graph Learning for Procedure-agnostic Instrument Tip Segmentation in Robotic Surgeries—0
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection—0
Where Did the Gap Go? Reassessing the Long-Range Graph BenchmarkCode1
Efficient Multi-View Graph Clustering with Local and Global Structure PreservationCode0
Scalable Incomplete Multi-View Clustering with Structure AlignmentCode1
CktGNN: Circuit Graph Neural Network for Electronic Design AutomationCode1
MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting—0
Structural Node Embeddings with Homomorphism Counts—0
Universal Graph Continual Learning—0
Class-Imbalanced Graph Learning without Class RebalancingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified