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 1326–1350 of 1570 papers

TitleStatusHype
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
Edge-Featured Graph Attention Network—0
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and CombinationsCode0
GraphHop: An Enhanced Label Propagation Method for Node ClassificationCode1
Topology-aware Tensor Decomposition for Meta-graph Learning—0
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
Wasserstein Coupled Graph Learning for Cross-Modal Retrieval—0
Light Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance—0
Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection—0
Online Discriminative Graph Learning from Multi-Class Smooth Signals—0
Incremental Learning on Growing Graphs—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
Graph Learning via Spectral Densification—0
Learning to Solve Multi-Robot Task Allocation with a Covariant-Attention based Neural Architecture—0
Inductive Collaborative Filtering via Relation Graph Learning—0
Algorithms for Learning Graphs in Financial MarketsCode0
Bosonic Random Walk Networks for Graph Learning—0
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks—0
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph LearningCode0
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model SegmentationCode1
High-Dimensional Bayesian Optimization via Tree-Structured Additive ModelsCode1
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
LCS Graph Kernel Based on Wasserstein Distance in Longest Common Subsequence Metric SpaceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified