Homophily, influence and the decay of segregation in self-organizing networks - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Network Science Year : 2016

Homophily, influence and the decay of segregation in self-organizing networks

Pawel Pralat
  • Function : Author
  • PersonId : 950365


We study the persistence of network segregation in networks characterized by the co-evolution of nodal attributes and link structures, in particular where individual nodes form linkages on the basis of similarity with other network nodes (homophily), and where nodal attributes diffuse across linkages, making connected nodes more similar over time (influence). A general mathematical model of these processes is used to examine the relative influence of homophily and influence in the maintenance and decay of network segregation in self-organizing networks. While prior work has shown that homophily is capable of producing strong network segregation when attributes are fixed, we show that adding even minute levels of influence is sufficient to overcome the tendency towards segregation even in the presence of relatively strong homophily processes. This result is proven mathematically for all large networks, and illustrated through a series of computational simulations that account for additional network evolution processes. This research contributes to a better theoretical understanding of the conditions under which network segregation and related phenomenon—such as community structure—may emerge, which has implications for the design of interventions that may promote more efficient network structures.
Fichier principal
Vignette du fichier
homophily_and_contagion.pdf (1.18 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01291960 , version 1 (22-03-2016)


  • HAL Id : hal-01291960 , version 1


Adam Douglas Henry, Dieter Mitsche, Pawel Pralat. Homophily, influence and the decay of segregation in self-organizing networks. Network Science, 2016. ⟨hal-01291960⟩


29 View
127 Download


Gmail Facebook X LinkedIn More