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 1451–1475 of 1570 papers

TitleStatusHype
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay—0
An Uncoupled Training Architecture for Large Graph Learning—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
A Survey on The Expressive Power of Graph Neural Networks—0
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance—0
Uncovering Insurance Fraud Conspiracy with Network Learning—0
Learning Product Graphs Underlying Smooth Graph Signals—0
PuzzleNet: Scene Text Detection by Segment Context Graph Learning—0
Dynamic Graph Learning based on Graph Laplacian—0
Learning graph representations of biochemical networks and its application to enzymatic link predictionCode0
Random Features Strengthen Graph Neural NetworksCode0
Efficient and Stable Graph Scattering Transforms via Pruning—0
Theoretically Expressive and Edge-aware Graph Learning—0
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised ClassificationCode0
Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data—0
Time-Varying Graph Learning with Constraints on Graph Temporal Variation—0
Neural Subgraph Isomorphism CountingCode0
Graph Learning Under Partial Observability—0
Video action detection by learning graph-based spatio-temporal interactionsCode0
Multi-view Subspace Clustering via Partition Fusion—0
Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders—0
Sparse Graph Attention NetworksCode0
Graph Agreement Models for Semi-Supervised Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified