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

Showing 5160 of 982 papers

TitleStatusHype
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
MultiGPrompt for Multi-Task Pre-Training and Prompting on GraphsCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical UnderstandingCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
UniMAP: Universal SMILES-Graph Representation LearningCode1
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation LearningCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
Certifiably Robust Graph Contrastive LearningCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Show:102550
← PrevPage 6 of 99Next →

Benchmark Results

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