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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 1570 papers

TitleStatusHype
Scalable Graph Generative Modeling via Substructure SequencesCode0
Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines—0
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression—0
Higher-order Structure Boosts Link Prediction on Temporal Graphs—0
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search—0
When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty—0
Lightweight Transformer via Unrolling of Mixed Graph Algorithms for Traffic ForecastCode0
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models—0
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement—0
Never Skip a Batch: Continuous Training of Temporal GNNs via Adaptive Pseudo-Supervision—0
Relation-Aware Graph Foundation Model—0
Multi-Attribute Graph Estimation with Sparse-Group Non-Convex Penalties—0
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach—0
Joint Graph Estimation and Signal Restoration for Robust Federated Learning—0
Unfolded Deep Graph Learning for Networked Over-the-Air Computation—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Learning Kronecker-Structured Graphs from Smooth Signals—0
Clustering of Incomplete Data via a Bipartite Graph Structure—0
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph LearningCode0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
Partial Label ClusteringCode0
Rethinking Federated Graph Learning: A Data Condensation Perspective—0
Multi-Scale Graph Learning for Anti-Sparse Downscaling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified