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 411–420 of 982 papers

TitleStatusHype
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
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Benchmark Results

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