LAUSR

MATHEMATICAL models of social networks, i.e., communities of interacting individuals, have existed for more than 50 years and have been used extensively by sociologists, behavioral scientists, and economists. The traditional focus has been on obtaining models that capture sociological effects like interpersonal influence (tendency of individuals to be influenced by others), homophily (tendency to associate with other individuals of similar behavior, opinions, and characteristics), polarization (tendency of a community to split into opposite factions), crowd effects (tendency to follow the opinion of the majority), echo chambers (tendency of an isolated community to self-amplify their beliefs), and so on. With the advent of online social media, the breadth and scope of the research in social network theory has scaled drastically in both size and accuracy, as numbers of interacting individuals have soared, and recorded data streams have rendered the analysis of individual behaviors, preferences, and interpersonal relationships more quantitative than ever before. Alongside the graph-theoretical notions drawn from network science, such as centrality, connectivity, and resilience, tools from dynamical systems and control prove very useful when trying to capture and understand the emerging behavior of such complex systems. Indeed, the last few years have witnessed a steep increase in the number of works studying social networks from a control systems perspective. This Special Issue of IEEE TRANSACTIONS