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 751–760 of 982 papers

TitleStatusHype
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation LearningCode1
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Retrieving Complex Tables with Multi-Granular Graph Representation LearningCode1
Graph Pooling via Coarsened Graph InfomaxCode0
UniGNN: a Unified Framework for Graph and Hypergraph Neural NetworksCode1
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
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
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Benchmark Results

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