SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 of 982 papers

TitleStatusHype
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Graph Representation learning for Audio & Music genre Classification—0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
Graph Representation Learning for Infrared and Visible Image Fusion—0
Graph Representation Learning for Interactive Biomolecule Systems—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Graph Representation Learning for Spatial Image Steganalysis—0
Graph representation learning for street networks—0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Graph Representation Learning Towards Patents Network Analysis—0
Graph Representation Learning via Contrasting Cluster Assignments—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Graph Representation Learning with Individualization and Refinement—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Hierarchical Prototype Network for Continual Graph Representation Learning—0
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
Hierarchical Transformer for Scalable Graph Learning—0
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
Hop-Hop Relation-aware Graph Neural Networks—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Identifying critical nodes in complex networks by graph representation learning—0
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach—0
Implications of sparsity and high triangle density for graph representation learning—0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
Inductive Graph Representation Learning with Quantum Graph Neural Networks—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Inferential SIR-GN: Scalable Graph Representation Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
Information propagation dynamics in Deep Graph Networks—0
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks—0
Instance-Aware Graph Prompt Learning—0
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks—0
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
JCapsR: 一种联合胶囊神经网络的藏语知识图谱表示学习模型(JCapsR: A Joint Capsule Neural Network for Tibetan Knowledge Graph Representation Learning)—0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach—0
KAN KAN Buff Signed Graph Neural Networks?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified