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 811–820 of 982 papers

TitleStatusHype
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
LMSOC: An approach for socially sensitive pretraining—0
Hierarchical Prototype Network for Continual Graph Representation Learning—0
A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets—0
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Graph Pooling via Coarsened Graph InfomaxCode0
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
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Benchmark Results

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