# 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} }

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…

## 2 Citations

### Improving Node Classification through Convolutional Networks Built on Enhanced Message-Passing Graph

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This article addresses the problem via building GCN on Enhanced Message-Passing Graph by utilizing dropout to extract a group of variants from the EMPG and then builds multichannel GCNs on them and demonstrates that the proposed method yields improvements in node classification.

### A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks

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This work considers the problem of quantifying the uncertainty in predictions of GNN stemming from modeling errors and measurement uncertainty, and proposes an approach to treat both sources of uncertainty in a Bayesian framework, where Assumed Density Filtering is used to quantify aleatoric uncertainty and Monte Carlo dropout captures uncertainty in model parameters.

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