Second-Order Assortative Mixing in Social Networks

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

DOI

View graph of relations

In a social network, the number of links of a node, or node degree, is often assumed as a proxy for the node’s importance or prominence within the network. It is known that social networks exhibit the (first-order) assortative mixing, i.e. if two nodes are connected, they tend to have similar node degrees, suggesting that people tend to mix with those of comparable prominence. In this paper, we report the second-order assortative mixing in social networks. If two nodes are connected, we measure the degree correlation between their most prominent neighbours, rather than between the two nodes themselves. We observe very strong second-order assortative mixing in social networks, often significantly stronger than the first-order assortative mixing. This suggests that if two people interact in a social network, then the importance of the most prominent person each knows is very likely to be the same. This is also true if we measure the average prominence of neighbours of the two people. This property is weaker or negative in non-social networks. We investigate a number of possible explanations for this property. However, none of them was found to provide an adequate explanation. We therefore conclude that second-order assortative mixing is a new property of social networks.
Original languageEnglish
Title of host publicationComplex Networks Viii : Proceedings of the 8th Conference on Complex Networks Complenet 2017
PublisherSpringer
Publication date2017
Pages3-15
ISBN (print)9783319542416
DOIs
StatePublished - 2017
Event8th Conference on Complex Networks Complenet 2017 - Dubrovnik, Croatia

Conference

Conference8th Conference on Complex Networks Complenet 2017
LocationInter University Center Dubrovnik
CountryCroatia
CityDubrovnik
Period21/03/201724/03/2017
SeriesSpringer Proceedings in Complexity
ISSN2213-8684
CitationsWeb of Science® Times Cited: 0

    Keywords

  • Physics, Applications of Graph Theory and Complex Networks, Computational Social Sciences, Computational Intelligence, Artificial Intelligence (incl. Robotics), Complexity
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

ID: 134358356