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 301–350 of 982 papers

TitleStatusHype
Material Prediction for Design Automation Using Graph Representation LearningCode0
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs EmbeddingCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Adaptive Sampling Towards Fast Graph Representation LearningCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
LASE: Learned Adjacency Spectral EmbeddingsCode0
Cycle Invariant Positional Encoding for Graph Representation LearningCode0
Cycle Representation Learning for Inductive Relation PredictionCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and PredictionCode0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised ClassificationCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Improving Attention Mechanism in Graph Neural Networks via Cardinality PreservationCode0
Calibrating and Improving Graph Contrastive LearningCode0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Adaptive Graph Representation Learning for Video Person Re-identificationCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Connector 0.5: A unified framework for graph representation learningCode0
From ChebNet to ChebGibbsNetCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hyperbolic Neural NetworksCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Frameless Graph Knowledge DistillationCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Geometric Scattering Attention NetworksCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Heterogeneous Deep Graph InfomaxCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
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Benchmark Results

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