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 1376–1400 of 1570 papers

TitleStatusHype
Graph Information Bottleneck for Subgraph RecognitionCode1
Embedding Words in Non-Vector Space with Unsupervised Graph LearningCode1
Learning Latent Interactions for Event classification via Graph Neural Networks and PMU Data—0
Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks—0
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization TheoryCode0
Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification—0
QR and LQ Decomposition Matrix Backpropagation Algorithms for Square, Wide, and Deep -- Real or Complex -- Matrices and Their Software ImplementationCode0
Extracting Summary Knowledge Graphs from Long DocumentsCode1
Implicit Graph Neural NetworksCode1
CatGCN: Graph Convolutional Networks with Categorical Node FeaturesCode0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
Lifelong Graph LearningCode1
Structured Graph Learning for Clustering and Semi-supervised Classification—0
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
Multi-view Graph Learning by Joint Modeling of Consistency and InconsistencyCode1
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint—0
Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples—0
Multivariate Relations Aggregation Learning in Social Networks—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Dynamic Emotion Modeling with Learnable Graphs and Graph Inception NetworkCode1
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph LearningCode1
Adversarial Bipartite Graph Learning for Video Domain AdaptationCode1
Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified