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 551560 of 982 papers

TitleStatusHype
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
Connector 0.5: A unified framework for graph representation learningCode0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning0
What Do GNNs Actually Learn? Towards Understanding their RepresentationsCode0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks0
Dynamic Graph Representation Learning with Neural Networks: A Survey0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
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Benchmark Results

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