Second-Order Assortative Mixing in Social Networks

Shi Zhou, Ingemar Cox, Lars Kai Hansen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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
Publication date2017
ISBN (Print)9783319542416
Publication statusPublished - 2017
Event8th Conference on Complex Networks Complenet 2017 - Inter University Center Dubrovnik, Dubrovnik, Croatia
Duration: 21 Mar 201724 Mar 2017


Conference8th Conference on Complex Networks Complenet 2017
LocationInter University Center Dubrovnik
SeriesSpringer Proceedings in Complexity


  • Physics
  • Applications of Graph Theory and Complex Networks
  • Computational Social Sciences
  • Computational Intelligence
  • Artificial Intelligence (incl. Robotics)
  • Complexity

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