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 651–675 of 1570 papers

TitleStatusHype
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node RepresentationCode1
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation—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
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
Infinite Width Graph Neural Networks for Node Regression/ ClassificationCode0
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
ResolvNet: A Graph Convolutional Network with multi-scale Consistency—0
DURENDAL: Graph deep learning framework for temporal heterogeneous networks—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified