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

Showing 341350 of 982 papers

TitleStatusHype
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Frameless Graph Knowledge DistillationCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Geometric Scattering Attention NetworksCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Heterogeneous Deep Graph InfomaxCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Show:102550
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Benchmark Results

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