NeighborNeighbor Correlations Explain Measurement Bias in Networks
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 neighborneighbor correlations, which are degree correlations one step beyond those of neighboring nodes, we accurately predict the impact of the strong friendship paradox in realworld networks. Understanding how the paradox biases local observations can inform better measurements of network structure and our understanding of collective phenomena.
 Publication:

Scientific Reports
 Pub Date:
 July 2017
 DOI:
 10.1038/s41598017060420
 arXiv:
 arXiv:1612.08200
 Bibcode:
 2017NatSR...7.5576W
 Keywords:

 Computer Science  Social and Information Networks;
 Condensed Matter  Statistical Mechanics;
 Computer Science  Computers and Society;
 Physics  Physics and Society