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 251–300 of 982 papers

TitleStatusHype
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation LearningCode0
About Graph Degeneracy, Representation Learning and ScalabilityCode0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
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
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Learning node representation via Motif CoarseningCode0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
A Deep Latent Space Model for Graph Representation LearningCode0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Calibrating and Improving Graph Contrastive LearningCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Heterogeneous Deep Graph InfomaxCode0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hyperbolic Neural NetworksCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
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Benchmark Results

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