Network Lens: Node Classification in Topologically Heterogeneous Networks

Kshiteesh Hegde; Malik Magdon-Ismail

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arXiv · 1901.09681

Network Lens: Node Classification in Topologically Heterogeneous Networks

Kshiteesh Hegde·Malik Magdon-Ismail

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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.

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Kshiteesh Hegde, Malik Magdon-Ismail. 2019-01-15. Network Lens: Node Classification in Topologically Heterogeneous Networks. https://arxiv.org/abs/1901.09681

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