Traditional social network analysis often models homophily, the tendency of similar individuals to form connections. using a single parameter. We will show that in many important applications, such as hypergraphs or temporal contact networks, homophily occurs at several different scales. We present a model that integrates these different homophily values through a random graph model with a maximum entropy approach. We demonstrate that the interaction between different levels of homophily has a non-trivial effect on percolation thresholds. Furthermore, we show that our model fits remarkably well on a wide range of data sets, capturing their homophily patterns accurately.

 Bio: Clara Stegehuis is an associate professor in applied mathematics at the University of Twente. She works at the intersection of probability theory, optimization and stochastic networks. She works on random graph models for various real-world problems related to for example epidemic spreading, network motifs, chemical reactions and cellular networks. Besides her mathematical work, she has a passion for science communication. She often presents the fascinating aspects of mathematics or networks at various places, from music festivals to primary schools.

Web page: https://www.clarastegehuis.nl/