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 531–540 of 982 papers

TitleStatusHype
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
OCTAL: Graph Representation Learning for LTL Model Checking—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
MM-GATBT: Enriching Multimodal Representation Using Graph Attention NetworkCode0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
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Benchmark Results

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