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 381390 of 982 papers

TitleStatusHype
Hierarchical Transformer for Scalable Graph Learning0
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningCode2
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
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Benchmark Results

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