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 831–840 of 982 papers

TitleStatusHype
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?Code0
LASE: Learned Adjacency Spectral EmbeddingsCode0
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient MatchingCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Graph Convolutional Networks with EigenPoolingCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
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Benchmark Results

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