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 321–330 of 982 papers

TitleStatusHype
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Connector 0.5: A unified framework for graph representation learningCode0
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Benchmark Results

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