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 226–250 of 982 papers

TitleStatusHype
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Reward Propagation Using Graph Convolutional NetworksCode1
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Bridging the Gap between Community and Node Representations: Graph Embedding via Community DetectionCode0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Do Transformers Really Perform Badly for Graph Representation?Code0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
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
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Benchmark Results

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