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 791–800 of 982 papers

TitleStatusHype
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Predicting Patient Outcomes with Graph Representation LearningCode1
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective—0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Learning Latent Topology for Graph Matching—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
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Benchmark Results

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