Structural Diversity and Homophily: A Study Across More than One Hundred Big Networks

February 23, 2016 Β· Declared Dead Β· πŸ› Knowledge Discovery and Data Mining

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Authors Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla arXiv ID 1602.07048 Category cs.SI: Social & Info Networks Cross-listed physics.soc-ph Citations 28 Venue Knowledge Discovery and Data Mining Last Checked 3 months ago
Abstract
A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.
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