Title
Multiscale Mixing Patterns In Networks
Abstract
Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks, manifesting as a higher tendency of links occurring between people of the same age, race, or political belief. Quantifying the level of assortativity or disassortativity (the preference of linking to nodes with different attributes) can shed light on the organization of complex networks. It is common practice to measure the level of assortativity according to the assortativity coefficient, or modularity in the case of categorical metadata. This global value is the average level of assortativity across the network and may not be a representative statistic when mixing patterns are heterogeneous. For example, a social network spanning the globe may exhibit local differences in mixing patterns as a consequence of differences in cultural norms. Here, we introduce an approach to localize this global measure so that we can describe the assortativity, across multiple scales, at the node level. Consequently, we are able to capture and qualitatively evaluate the distribution of mixing patterns in the network. We find that, for many real-world networks, the distribution of assortativity is skewed, overdispersed, and multimodal. Our method provides a clearer lens through which we can more closely examine mixing patterns in networks.
Year
DOI
Venue
2017
10.1073/pnas.1713019115
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Keywords
Field
DocType
complex networks, assortativity, multiscale, node metadata
Assortative mixing,Assortativity,Social network,Statistic,Computer science,Categorical variable,Mixing patterns,Theoretical computer science,Complex network,Artificial intelligence,Modularity,Machine learning
Journal
Volume
Issue
ISSN
115
16
0027-8424
Citations 
PageRank 
References 
3
0.40
12
Authors
3
Name
Order
Citations
PageRank
Leto Peel11177.06
Jean-Charles Delvenne229932.41
Renaud Lambiotte392064.98