Estimation of the Sample Frechet Mean: A Convolutional Neural Network Approach
This provides a novel tool for statistical and machine learning with graph data, addressing a specific bottleneck in computing means for networks.
The authors tackled the problem of computing the sample Fréchet mean for graph-valued random variables by proposing a fast algorithm using convolutional neural networks to learn graph morphology, and their experiments on random graph ensembles showed reliable recovery of the mean.
This work addresses the rising demand for novel tools in statistical and machine learning for "graph-valued random variables" by proposing a fast algorithm to compute the sample Frechet mean, which replaces the concept of sample mean for graphs (or networks). We use convolutional neural networks to learn the morphology of the graphs in a set of graphs. Our experiments on several ensembles of random graphs demonstrate that our method can reliably recover the sample Frechet mean.