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 351360 of 982 papers

TitleStatusHype
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Local2Global: Scaling global representation learning on graphs via local trainingCode0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
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Benchmark Results

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