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https://doi.org/10.2139/ssrn.4663055
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.ins.2023.119617openalex-extracted
Search for deep graph neural networks
inferredcites →https://doi.org/10.1016/j.neunet.2022.10.001openalex-extracted
Multi-Aspect enhanced Graph Neural Networks for recommendation
inferredcites →https://doi.org/10.1016/j.neunet.2023.05.048openalex-extracted
DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks
inferredcites →https://doi.org/10.1016/j.neunet.2023.10.052openalex-extracted
Revisiting multi-view learning: A perspective of implicitly heterogeneous Graph Convolutional Network
inferredcites →https://doi.org/10.1016/j.patcog.2019.107000openalex-extracted
Dynamic graph convolutional networks
inferredcites →https://doi.org/10.1016/j.patcog.2023.109537openalex-extracted
Line graph contrastive learning for link prediction
inferredcites →https://doi.org/10.1109/tbdata.2022.3177455openalex-extracted
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
inferredcites →https://doi.org/10.1109/tii.2020.3011441openalex-extracted
A Conditional Convolutional Autoencoder-Based Method for Monitoring Wind Turbine Blade Breakages
inferredcites →https://doi.org/10.1109/tkde.2020.3014089openalex-extracted
Learning Deep Graph Representations via Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/tkde.2021.3131584openalex-extracted
Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive
inferredcites →https://doi.org/10.1109/tkde.2022.3142179openalex-extracted
Multi-View Tensor Graph Neural Networks Through Reinforced Aggregation
inferredcites →https://doi.org/10.1109/tkde.2022.3172903openalex-extracted
Graph Self-Supervised Learning: A Survey
inferredcites →https://doi.org/10.1109/tnnls.2019.2921841openalex-extracted
GANE: A Generative Adversarial Network Embedding
inferredcites →https://doi.org/10.1109/tnnls.2020.2978386openalex-extracted
A Comprehensive Survey on Graph Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2021.3080635openalex-extracted
Line Graph Neural Networks for Link Prediction
inferredcites →https://doi.org/10.1109/tpami.2022.3170559openalex-extracted
Self-Supervised Learning of Graph Neural Networks: A Unified Review
inferredcites →https://doi.org/10.1111/j.1468-0297.2008.02139.xopenalex-extracted
Optimal Degree of Public Information Dissemination
inferredcites →https://doi.org/10.1145/3442381.3449802openalex-extracted
Graph Contrastive Learning with Adaptive Augmentation
inferredcites →https://doi.org/10.1145/3485447.3512069openalex-extracted
Graph Neural Networks Beyond Compromise Between Attribute and Topology
inferredcites →https://doi.org/10.1145/3534678.3542609openalex-extracted
Graph Neural Networks: Foundation, Frontiers and Applications
inferredcites →https://doi.org/10.1145/3568022openalex-extracted
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
inferredcites →https://doi.org/10.1146/annurev.so.06.080180.000455openalex-extracted
Models of Network Structure
inferredcites →https://doi.org/10.1186/s40649-019-0069-yopenalex-extracted
Graph convolutional networks: a comprehensive review
inferredcites →https://doi.org/10.1609/aaai.v37i9.26336openalex-extracted
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-extracted
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.4230/lipics.giscience.2025.9openalex-extracted
Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach
inferredcites →https://doi.org/10.4230/lipics.sea.2024.20openalex-extracted
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1706.02216openalex-extracted
Inductive Representation Learning on Large Graphs
inferredcites →https://doi.org/10.48550/arxiv.1902.07153openalex-extracted
Simplifying Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.2006.04131openalex-extracted
Deep Graph Contrastive Representation Learning
inferredcites →https://doi.org/10.48550/arxiv.2006.05582openalex-extracted
Contrastive Multi-View Representation Learning on Graphs
inferredcites →https://doi.org/10.48550/arxiv.2010.13902openalex-extracted
Graph Contrastive Learning with Augmentations
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