Images can be powerful; and, as the saying goes, “with great power comes great responsibility.” Today, the world is suffused with images through various media, and people have come to expect pictures to tell them stories. With increased computational power, images of quantitative data are increasingly part of the “stories” one commonly sees and are powerful in communicating research findings. Many of these images are informative and effective; others are confusing, convey little actual information, or, sadly, are used to intentionally mislead for ideological reasons. Network science has always used compelling images to tell stories about structures, and the field is therefore particularly suited to make the most use of this era of data visualization. But given the vastly expanded palette of visualization available today, how does the researcher decide what is a good network image?

Where do networks come from? Numerous theories direct us to the causes of networks (e.g., homophily, triadic closure, physical proximity), some emphasizing outside factors (exogenous causes) and others emphasizing point-in-time network structure (endogenous causes) as shaping a network’s future trajectory. So far, we have examined such causal theories using cross-sectional snapshots in the form of metrics (centrality, density), partitions (clusters), and maps or spaces (visualization). These approaches generally suffer from a lack of stochastic features and observational overdetermination: for example, we observe a pattern in a given school on a given day, but that pattern could result from actor preferences and constraints in the setting. Disentangling such effects requires an inferential approach to probabilistically examine various effects. To the extent that we want to identify causal forces shaping the networks, understanding the unfolding of relations in time – how the individual ties in a network (the dyads joined by one or more relations) and the entire structure of these relations emerge and evolve – is crucial for testing network theories.

Stop. Take a moment to look around. What do you see? No matter where you are, you are likely perceiving a world consisting of things. Maybe you are reading this book in a coffee shop, and if so, you probably see people, cups, books, chairs, and so on. You see a world of objects with properties, yourself included: white cups are on wooden tables, people sitting in chairs are reading books and talking with one another. At the same time, you are a subject, responding to this world and actively bringing yourself and these objects into interrelation. And yet, the world of objects with properties that you are perceiving is but one slice of a complex reality.