Neighbor-Neighbor Correlations Explain Measurement Bias in Networks

Xin-Zeng Wu; Allon G. Percus; Kristina Lerman

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arXiv · 1612.08200

Neighbor-Neighbor Correlations Explain Measurement Bias in Networks

Xin-Zeng Wu·Allon G. Percus·Kristina Lerman

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Abstract

In numerous physical models on networks, dynamics are based on interactions that exclusively involve properties of a node's nearest neighbors. However, a node's local view of its neighbors may systematically bias perceptions of network connectivity or the prevalence of certain traits. We investigate the strong friendship paradox, which occurs when the majority of a node's neighbors have more neighbors than does the node itself. We develop a model to predict the magnitude of the paradox, showing that it is enhanced by negative correlations between degrees of neighboring nodes. We then show that by including neighbor-neighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in real-world networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.

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cs.SI cond-mat.stat-mech cs.CY physics.soc-ph

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Xin-Zeng Wu, Allon G. Percus, Kristina Lerman. 2016-12-24. Neighbor-Neighbor Correlations Explain Measurement Bias in Networks. https://arxiv.org/abs/1612.08200

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