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 526–550 of 982 papers

TitleStatusHype
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Towards Graph Representation Learning Based Surgical Workflow AnticipationCode0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
OCTAL: Graph Representation Learning for LTL Model Checking—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Model-Aware Contrastive Learning: Towards Escaping the DilemmasCode0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
MM-GATBT: Enriching Multimodal Representation Using Graph Attention NetworkCode0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Causal Machine Learning: A Survey and Open Problems—0
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
A Representation Learning Framework for Property GraphsCode1
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction—0
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Transferable Graph Backdoor Attack—0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Boosting Graph Structure Learning with Dummy NodesCode1
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Benchmark Results

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