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 101–110 of 982 papers

TitleStatusHype
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
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Benchmark Results

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