The concept of a network path has been foundational to network scholarship, enabling measures like betweenness, centrality, or reachability to capture how social actors connect and how information flows in a network. Advances in communication technology and the availability of fine grained time stamped interactions data necessitate rethinking the concept of network path to enable the development of measures, metrics and concepts uniquely adapted to capture the dynamics of social interactions. Building on the increasing recognition of the importance of microdynamics in social life, we advocate extending the notion of a network path into the temporal domain. We propose a framework for studying temporal structural dynamics in social networks, introducing metrics and measures tailored to capture the intricacies of ephemeral, time-sensitive relational processes. By integrating temporal dimensions, scholars can explore not only how and whether social actors connect, but also the temporal nuances of their interactions. This dynamic approach has profound theoretical implications for understanding relational processes and offers novel methodological tools for analyzing the evolving nature of human networks.

It is increasingly common to study mobility and migration of individuals between social and physical locations as networks in which locations are nodes connected by mobile individuals. This conceptualisation as mobility networks facilitates the analysis of how individuals influence one another in their mobility destinations.

Technically, this amounts to analysing interdependence between individuals' mobility paths. A recently proposed framework allows the statistical modelling of these social processes and, therefore, dependence in mobility, combining features of Exponential Random Graph Models (ERGMs) and classic log-linear models.

However, insufficient attention was paid to how such models should be specified in a principled, theoretically informed way. In this presentation, we apply statistical theory to propose model specifications that can be used to analyse emergent structures in mobility. In particular, we discuss how to specify models that (i) are based on clear dependence assumptions on the individual level, that (ii) have a clear individual level interpretation, that (iii) avoid (near-)degeneracy, a common problem for models with dependent observations, and (iv) that may be used to carry out population inference with sampled data.

Relational event models (REMs) describe temporal interactions between social actors, from the invasions of alien species, lending on the interbank market and patient transfers between hospitals. An important question in all of these, is what are the causal drivers underlying these processes. This is a causal discovery question and requires a causal inferential procedure that goes beyond MLE-based associative inference currently employed for REMs.

By exploiting the connection between REM partial likelihood under nested-case control sampling and logistic regression, we are able to use causal discovery methods recently developed for generalized linear models. In particular, we show that a causal relational event model is identified by means of two conditions: a standard MLE property and a crucial invariance property, expressed in terms of Pearson risk. The empirical analogue of these conditions requires a statistical test for the detection of the causal model among a set of candidate models. For this, we develop an efficient alternative to bootstrap, by deriving an approximate distribution of the standardized Pearson statistic under the causal model. We apply this method on a dataset of 350,000 events to investigate the causal drivers of bike sharing in Washington D.C.

Imagine a social network and most likely you will conjure up a picture of a static graph. In fact, this is how network models started to be developed by foundational work in the 1970s, followed in the 1980s by exponential random graph models. These models model the probability of the presence of an edge, possibly dependent on other edges. When temporal network data started to become available in the 1990s, an obvious choice was the temporal ERGM extension. Obvious, but not necessarily sensible. In fact, in the early 2000s SAOMs proposed modelling the presence of an edge not as a binary process, but as a temporal event history process. Pushing the connection with non-homogeneous Poisson process further, the relational event model (REM) introduced in 2008 has proven a particularly flexible model for modelling all kinds of relational processes, from alien species invasions, bike sharing, communication networks to lending on interbank markets.

In this talk I will sketch the most important developments in the history of dynamic network modelling, including TERGMS, SAOMs and Dynams, with a particular attention to relational event models. We describe the five-fold temporal dependence of relational events on time, and we show how new developments in REMs make the model ever more practical and adaptable to the needs of a practitioner.

Network data are widely collected across various domains, from biology to social sciences. The most common representation of these data is in the form of dyadic relationships, where pairs of nodes are connected by links. However, in some cases, groups of people are linked by higher-order interactions, such as two persons gossiping about a third one. Hypergraphs provide a framework for such interactions, where hyperedges represent subsets of nodes participating in the same event.

This work presents an extension of dynamic network models to higher-order interactions. By modeling hyper events via the proposed relational event model, we are able to analyze the temporal evolution of hyperedges and to uncover the dynamics of complex social phenomena. Using a rich dataset from a longitudinal study of over 40 secondary school communities in Hungary, we explore the dynamics of gossiping and the drivers underlying this social phenomenon. In this way, we are able to uncover patterns in how gossiping forms and evolves, offering new insights into social dynamics.