Abstract

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 results in complex percolation thresholds. Furthermore, we show that our model fits remarkably well on a wide range of data sets, capturing their homophily patterns accurately.

Speaker's Bio

Clara Stegehuis is an associate professor at the University of Twente. She works at the intersection of probability theory, graph theory and stochastic networks, with an emphasis on asymptotic analysis, stochastic process limits, and randomized algorithms. Problems she investigates are often inspired by applications in network science, physics and computer science.