Network Lens: Node Classification in Topologically Heterogeneous Networks
Social and Information Networks
2019-01-29 v1 Machine Learning
Machine Learning
Abstract
We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network. We zoom in to the network using lenses of different sizes to capture the local structure of the network. These network signatures are then weighted to provide a set of predicted labels for every node. We achieve a peak accuracy of (random=) on two networks with and nodes each. Further, we perform better than random even when the given node is connected to up to 5 different types of networks. Finally, we perform this analysis on homogeneous networks and show that highly structured networks have high homogeneity.
Cite
@article{arxiv.1901.09681,
title = {Network Lens: Node Classification in Topologically Heterogeneous Networks},
author = {Kshiteesh Hegde and Malik Magdon-Ismail},
journal= {arXiv preprint arXiv:1901.09681},
year = {2019}
}