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 751–775 of 1570 papers

TitleStatusHype
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
Euclidean geometry meets graph, a geometric deep learning perspective—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters—0
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
Are Large Language Models In-Context Graph Learners?—0
Equivariant Polynomials for Graph Neural Networks—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
CNN2GNN: How to Bridge CNN with GNN—0
Entailment Graph Learning with Textual Entailment and Soft Transitivity—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Clustering with Similarity Preserving—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
Are Hyperbolic Representations in Graphs Created Equal?—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data—0
Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics—0
End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Architectural Implications of Embedding Dimension during GCN on CPU and GPU—0
Ember: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified