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 476–500 of 982 papers

TitleStatusHype
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