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 1351–1375 of 1570 papers

TitleStatusHype
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Graph Intention Network for Click-through Rate Prediction in Sponsored Search—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Expanding Semantic Knowledge for Zero-shot Graph Embedding—0
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link PredictionCode0
Recognizing Predictive Substructures with Subgraph Information Bottleneck—0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Graph Force Learning—0
Online Graph Learning under Smoothness Priors—0
Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition—0
Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification—0
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional NetworksCode0
GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks—0
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view—0
Pre-demosaic Graph-based Light Field Image Compression—0
A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection—0
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and CombinationsCode0
Edge-Featured Graph Attention Network—0
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval—0
Topology-aware Tensor Decomposition for Meta-graph Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified