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 841–850 of 982 papers

TitleStatusHype
Disentangle-based Continual Graph Representation LearningCode1
NodeSig: Binary Node Embeddings via Random Walk Diffusion—0
Multi-hop Attention Graph Neural NetworkCode1
Information Obfuscation of Graph Neural NetworksCode1
Sub-graph Contrast for Scalable Self-Supervised Graph Representation LearningCode1
div2vec: Diversity-Emphasized Node Embedding—0
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
Polyp-artifact relationship analysis using graph inductive learned representations—0
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks—0
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Benchmark Results

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