2019/10/21 by Xin-Zeng Wu, Wu, Xin-Zeng, Allon G. Percus +5
Mathematics · Physics and Astronomy · Psychology · #Assortativity #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer network #Computer science #Degree (music) #Degree distribution #FOS: Physical sciences #Mathematics #Mental Health Research Topics #Network topology #Opinion Dynamics and Social Influence #Physics #Physics and Society (physics.soc-ph) #Property (philosophy) #Statistical Mechanics (cond-mat.stat-mech) #Theoretical computer science #Topology (electrical circuits) #cond-mat.stat-mech #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1910.09538
published in arXiv (Cornell University) (Cornell University) · 6 pages, 5 figures
arxiv created 2019/10/21 · openalex publication_date 2019/10/21 · arxiv updated 2019/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Network topologies can be non-trivial, due to the complex underlying behaviors that form them. While past research has shown that some processes on networks may be characterized by low-order statistics describing nodes and their neighbors, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We introduce a property called transsortativity that describes correlations among a node's neighbors, generalizing these statistics from immediate one-hop neighbors to two-hop neighbors. We describe how transsortativity can be systematically varied, independently of the network's degree distribution and assortativity. Moreover, we show that it can significantly impact the spread of contagions as well as the perceptions of neighbors, known as the majority illusion. Our work improves our ability to create and analyze more realistic models of complex networks.