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 401–425 of 1570 papers

TitleStatusHype
CNN-based Dual-Chain Models for Knowledge Graph Learning—0
Are Large Language Models In-Context Graph Learners?—0
Federated Graph Learning with Graphless Clients—0
CNN2GNN: How to Bridge CNN with GNN—0
Are Hyperbolic Representations in Graphs Created Equal?—0
Clustering with Similarity Preserving—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
H^2GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs—0
Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy—0
Federated Learning with Graph-Based Aggregation for Traffic Forecasting—0
FedGL: Federated Graph Learning Framework with Global Self-Supervision—0
A Comprehensive Survey of Foundation Models in Medicine—0
Federated Graph Learning -- A Position Paper—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Architectural Implications of Embedding Dimension during GCN on CPU and GPU—0
Federated Graph Learning for Cross-Domain Recommendation—0
Characterizing the Influence of Topology on Graph Learning Tasks—0
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
A Primer on Temporal Graph Learning—0
Federated Graph Condensation with Information Bottleneck Principles—0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks—0
Causal Discovery on Dependent Binary Data—0
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified