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 851–875 of 1570 papers

TitleStatusHype
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
A Topology-aware Graph Coarsening Framework for Continual Graph Learning—0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNsCode0
Joint Signal Recovery and Graph Learning from Incomplete Time-Series—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
Diffusion Maps for Signal Filtering in Graph Learning—0
Dynamic Frequency Domain Graph Convolutional Network for Traffic ForecastingCode0
Multi-level graph learning for audio event classification and human-perceived annoyance rating predictionCode0
Robust Graph Neural Network based on Graph DenoisingCode0
TransGlow: Attention-augmented Transduction model based on Graph Neural Networks for Water Flow Forecasting—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—0
Learning High-Dimensional Differential Graphs From Multi-Attribute Data—0
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting—0
PerCNet: Periodic Complete Representation for Crystal Graphs—0
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning—0
Solve Large-scale Unit Commitment Problems by Physics-informed Graph Learning—0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified