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 751–775 of 982 papers

TitleStatusHype
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
Do Transformers Really Perform Badly for Graph Representation?Code0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Bridging the Gap between Community and Node Representations: Graph Embedding via Community DetectionCode0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
MGC: A Complex-Valued Graph Convolutional Network for Directed GraphsCode0
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningCode0
Graph Transformer for Graph-to-Sequence LearningCode0
Graph Representation Learning via Ladder Gamma Variational AutoencodersCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time SpansCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Point-Voxel Absorbing Graph Representation Learning for Event Stream based RecognitionCode0
PolyFormer: Scalable Node-wise Filters via Polynomial Graph TransformerCode0
What Do GNNs Actually Learn? Towards Understanding their RepresentationsCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
Positional Encoding meets Persistent Homology on GraphsCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State PredictionCode0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
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Benchmark Results

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