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 801–825 of 982 papers

TitleStatusHype
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
A Deep Latent Space Model for Graph Representation LearningCode0
ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural NetworksCode0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
Node Classification Meets Link Prediction on Knowledge Graphs—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Deep Learning on Graphs for Natural Language Processing—0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features—0
LMSOC: An approach for socially sensitive pretraining—0
Hierarchical Prototype Network for Continual Graph Representation Learning—0
A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets—0
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural NetworksCode0
Graph Pooling via Coarsened Graph InfomaxCode0
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
Unsupervised Deep Manifold Attributed Graph EmbeddingCode0
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
Graph Representation Learning in Biomedicine—0
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Benchmark Results

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