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 741–750 of 982 papers

TitleStatusHype
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Deep Learning on Graphs for Natural Language Processing—0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
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
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Benchmark Results

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