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 326–350 of 1570 papers

TitleStatusHype
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
A Structural Feature-Based Approach for Comprehensive Graph Classification—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Modeling Multi-Step Scientific Processes with Graph Transformer Networks—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy—0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization—0
Deep Generative Models for Subgraph PredictionCode0
Knowledge Probing for Graph Representation Learning—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph LearningCode0
Non-convolutional Graph Neural NetworksCode1
DyGKT: Dynamic Graph Learning for Knowledge TracingCode1
Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction—0
RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph LearningCode0
Gene Regulatory Network Inference from Pre-trained Single-Cell Transcriptomics Transformer with Joint Graph Learning—0
Lifelong Graph Learning for Graph SummarizationCode0
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction—0
Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation—0
Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency—0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Show:102550
← PrevPage 14 of 63Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified