Author Description

"Social processes operate at many temporal scales: decades, years, months, weeks, days, hours, minutes, and even seconds. With the advent of the Internet, social media, and other technologies, long sequences of time-series data are increasingly available at very fine scales (e.g., an hour of second-by-second recordings produces 3,600 data points; a day of minute-by-minute time-stamped information yields 1,440 data points).In Recurrence-Based Analyses, Sebastian Wallot and Giuseppe Leonardi introduce techniques developed in physics and physiology for characterizing and analyzing patterns in long sequences of temporal data to a broad audience of social scientists. In contrast to time-series regression and other related techniques, recurrence quantification analysis (RQA) arises in the context of chaos and nonlinear dynamical systems theory-theory arguably very relevant to social processes. The goal of Recurrence-Based Analyses is to characterize the system's complexity, stability and instability, and conditions under which it transitions from one state to another. The volume opens with an engaging example, a short poem for children entitled "Popcorn" by Helen H. Moore. Although the poem is not a time series per se, it is an ordered sequence of values (letters) that can be seen in this way. Professors Wallot and Leonardi use the repeating sound patterns in this poem to illustrate the concept of recurrence, the construction of a recurrence plot, and a variety of measures that quantify characteristics of this plot. The poem is short, with lots of rhyme and repetition (pop, pot, hot, top, stop). The recurrence plot, a matrix of the cross comparison of the values of a time series (in this case, letters of the poem), is wonderfully visual. Many measures can be calculated from the recurrence plot, which enables the reader to relate their values to the patterns they can (literally) see in the plot. This first chapter is accessible to all readers. It provides the foundation fo