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 251–275 of 982 papers

TitleStatusHype
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
About Graph Degeneracy, Representation Learning and ScalabilityCode0
Distill2Vec: Dynamic Graph Representation Learning with Knowledge DistillationCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
A Deep Latent Space Model for Graph Representation LearningCode0
Heterogeneous Deep Graph InfomaxCode0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
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Benchmark Results

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