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- W2913663242 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." @default.
- W2913663242 created "2019-02-21" @default.
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- W2913663242 date "2019-01-15" @default.
- W2913663242 modified "2023-09-27" @default.
- W2913663242 title "Network Lens: Node Classification in Topologically Heterogeneous Networks." @default.
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