# Network Lens: Node Classification in Topologically Heterogeneous Networks

## TL;DR

This paper introduces a method called Network Lens for node classification in large, topologically heterogeneous networks by analyzing local structures with variable-sized lenses, achieving significant accuracy improvements.

## Contribution

The paper presents a novel local-structure-based node classification approach that outperforms random guessing in complex heterogeneous networks.

## Key findings

- Achieved up to 42% accuracy on large networks
- Performed better than random with nodes connected to multiple network types
- Identified high homogeneity in highly structured networks

## 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 $\sim42\%$ (random=$11\%$) on two networks with $\sim100,000$ and $\sim1,000,000$ 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.