Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs
Machine Learning
2012-07-31 v1 Statistics Theory
Statistics Theory
Abstract
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the -nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia.
Cite
@article{arxiv.1207.6745,
title = {Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs},
author = {Daniel L. Sussman and Minh Tang and Carey E. Priebe},
journal= {arXiv preprint arXiv:1207.6745},
year = {2012}
}