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 51–100 of 1570 papers

TitleStatusHype
Dynamically Expandable Graph Convolution for Streaming RecommendationCode1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic NetworksCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
State of the Art and Potentialities of Graph-level LearningCode1
Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order InteractionsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Disentangled Condensation for Large-scale GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
CktGNN: Circuit Graph Neural Network for Electronic Design AutomationCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
GLAMOUR: Graph Learning over Macromolecule RepresentationsCode1
Adversarial Bipartite Graph Learning for Video Domain AdaptationCode1
CaseLink: Inductive Graph Learning for Legal Case RetrievalCode1
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge TransferCode1
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data AugmentationsCode1
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural NetworkCode1
DE-HNN: An effective neural model for Circuit Netlist representationCode1
Deep Iterative and Adaptive Learning for Graph Neural NetworksCode1
Cluster-wise Graph Transformer with Dual-granularity Kernelized AttentionCode1
Deep Temporal Graph ClusteringCode1
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement PredictionCode1
Diffusion Improves Graph LearningCode1
Dynamic Attentive Graph Learning for Image RestorationCode1
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New BenchmarkCode1
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and ReasoningCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Automating Botnet Detection with Graph Neural NetworksCode1
Covariant Compositional Networks For Learning GraphsCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
Convolutional Neural Networks on Graphs with Chebyshev Approximation, RevisitedCode1
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and DirectionsCode1
AutoGL: A Library for Automated Graph LearningCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Automatic Relation-aware Graph Network ProliferationCode1
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022Code1
Bilinear Scoring Function Search for Knowledge Graph LearningCode1
An Empirical Evaluation of Temporal Graph BenchmarkCode1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"Code1
An Influence-based Approach for Root Cause Alarm Discovery in Telecom NetworksCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
CaT: Balanced Continual Graph Learning with Graph CondensationCode1
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability DetectionCode1
Uncertainty-based graph convolutional networks for organ segmentation refinementCode1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HaloGraphNetR^20.97—Unverified