Signs over time: Statistical and visual analysis of a longitudinal signed network

This paper presents the design and results of a statistical and visual analysis of a dynamic signed network. In addition to prevalent approaches to longitudinal networks, which analyze series of cross-sectional data, this paper focuses on network data measured in continuous time in order to explain the signs of lines rather than their occurrence. As a consequence, current stochastic actor-oriented models for network change cannot be applied. Instead, multilevel logistic regression analysis is used for uncovering the main statistical regularities of network evolution. Visualization by means of animated Scalable Vector Graphics with several options for interaction allows for in-depth inspection of network evolution and offers detailed information on the people involved in the network.
The substantive focus of the paper is on the evaluations and complex labeling process among literary authors and critics illustrating the interplay between identity (literary style) and structure. It is hypothesized that actors do not just evaluate their immediate ego-network; they also try to survey and interpret the overall structure of the network and derive part of their identity from it. The latter, however, is a collective process involving communication, e.g., publicly labeling groups of actors in the network and adapting behavior to labels that have previously been assigned to actors. Including perceptions of overall network structure and classifications in dynamic network models would extend current actor-oriented models.