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

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Showing 261–270 of 982 papers

TitleStatusHype
Node Embeddings via Neighbor Embeddings—0
Inductive Graph Representation Learning with Quantum Graph Neural Networks—0
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Multi-View Node Pruning for Accurate Graph Representation—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
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Benchmark Results

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