ABSTRACT. How quickly is a disease spreading through a social network? How diversified is a country in a network of related exported products? How polarized is the online discourse on social media? These seemingly unrelated questions can be answered with a common toolbox: they all require to compare different types of node attributes. In this workshop, you will learn how to apply Node Vector Distance techniques: given two node attributes -- represented as vectors -- how far the two are on a network structure? Are they correlated when we take into account not only the value of a node itself, but also the values of its neighbors? Or simply: how spread out in the network is an attribute? The workshop will make you familiar with ways to translate concepts such as Euclidean distance, Pearson correlation, and variance, when the vectors do not live in an Euclidean space, but on the complex landscape that is the topology of a network.

ABSTRACT. Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform’s algorithms direct who sees which message. This tutorial will review the state of the art in modelling information diffusion in social media on multiple platforms and discuss challenges and opportunities for modeling such processes in diverse contexts, from political crises to coordinated disinformation campaigns.