Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks

@article{Komanduri2021NeighborhoodRW,
  title={Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks},
  author={Aneesh Komanduri and Justin Zhijun Zhan},
  journal={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2021},
  pages={903-908}
}
  • Aneesh KomanduriJ. Zhan
  • Published 1 December 2021
  • Computer Science
  • 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)
In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph are interconnected. The Graph Neural Network (GNN) has proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and… 
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