Data Mining in Big Dynamic Networks

Many automatic monitoring systems generate big dynamic network data, also called relational data: from invasive species diffusion across the globe (10-100K), bike-sharing rides between bike stations (100K-1M) to patent citations of novel technologies (10M-100M). The aim in analysing these data is typically to discover what drives the interactions to find effective strategies, respectively, to control invasive species, to predict bike sharing at any location at any time, to develop technological innovation.

This workshop explores the advancements in relational event modelling (REM) within the context of time-stamped relational data, commonly generated by email exchanges and social media interactions. The session begins with an introduction to REMs, emphasizing their application in identifying drivers of processes involving temporally ordered events. It delves into the extension of traditional network statistics in REMs, encompassing degree-based metrics and intensity-based counterparts, along with distinguishing short- and long-term network dynamics. The workshop progresses to mixed effect additive REMs, demonstrating how to integrate non-linear specifications and time-varying covariate influences. Reciprocity and triadic effects are revisited with a focus on dynamic structures, challenging assumptions of stability over time. Global covariates, previously challenging in traditional REMs, are addressed, allowing the inclusion of factors like weather or time-of-day. The workshop concludes by exploring strategies to efficiently apply REMs to huge datasets, overcoming computational complexities in ways that involve modern machine learning techniques.

Each session involves a concise explanatory segment followed by an extensive hands-on computer practical, encouraging participants to bring their laptops with Rstudio pre-installed.


  1. Introduction to Dynamic Networks
  2. Mixed Additive Relational Event Models


  1. Simulation of dynamic social networks
  2. Inference of dynamic social networks
  3. Nonlinear effects in dynamic social networks

Data needed for the practicals:

  1. Patent citation data
  2. Manufacturing company emails
  3. WTC raw
  4. WTC preprocessed
  5. Classroom raw
  6. Classroom preprocessed

Solutions practicals:

  1. Tutorial 1
  2. Tutorial 2
  3. Tutorial 3