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 1126–1150 of 1570 papers

TitleStatusHype
Disentangled Generative Graph Representation Learning—0
Disentangled Motif-aware Graph Learning for Phrase Grounding—0
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Distilling Large Language Models for Text-Attributed Graph Learning—0
Distributed Graph Learning with Smooth Data Priors—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction—0
Distribution Preserving Graph Representation Learning—0
A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
Do graph neural network states contain graph properties?—0
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm—0
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization—0
A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks—0
Do We Really Need Complicated Model Architectures For Temporal Networks?—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Dual Adversarial Perturbators Generate rich Views for Recommendation—0
Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection—0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks—0
Dual Space Graph Contrastive Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified