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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–300 of 982 papers

TitleStatusHype
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
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
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
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
A Deep Latent Space Model for Graph Representation LearningCode0
Calibrating and Improving Graph Contrastive LearningCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Hyperbolic Neural NetworksCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Heterogeneous Deep Graph InfomaxCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Show:102550
← PrevPage 6 of 20Next →

Benchmark Results

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