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 76–100 of 1570 papers

TitleStatusHype
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNsCode1
Plain Transformers Can be Powerful Graph Learners—0
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Trajectory Encoding Temporal Graph NetworksCode0
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures—0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives—0
Federated Prototype Graph Learning—0
NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed GraphCode1
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph LearningCode0
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey—0
Scalable Hypergraph Structure Learning with Diverse Smoothness PriorsCode0
Toward General and Robust LLM-enhanced Text-attributed Graph Learning—0
Alleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution—0
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive ReviewCode0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation—0
Rethinking Graph Structure Learning in the Era of LLMs—0
AdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram ModelCode0
Graph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models—0
Data-centric Federated Graph Learning with Large Language Models—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on GraphsCode2
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified