Understanding the Mechanisms that Drive Relational Events Dynamics in Corruption Networks

• In Event: EUROC 7 - Corruption and the Private Sector

Abstract

Recent advances in collecting and storing criminal network data allow us to examine their dynamics with considerable granularity – at the level of relational events. In relational event data, every single interaction between actors has a specific time-stamp indicating when it was formed.
Utilising publicly available data on three dynamic corporate corruption networks from Deferred Prosecution Agreements in the UK, we test the mechanisms that drive their evolution by modelling the sequence of relational events that gave it rise with recently developed relational hyper-event model, an extension to relational event models (RHEM) for polyadic events (i.e., events containing more than two actors). In RHEM, events are modelled as hyperedges in a hypergraph allowing to connect multiple nodes simultaneously.
We focus on two broader research questions. The first one concerns the macro-level structure of the networks and their overall evolution over time. Specifically, we test core-periphery structure fit and measure temporal escalation. Using the RHEM, we test the relational micro-mechanisms that bring about these structures. We include attribute-related mechanisms (selection, heterophily), hyperevent-specific endogenous mechanisms (interaction repetition, subordination), and general endogenous mechanisms (triadic closure, reciprocity, tie accumulation). Our results show that all three networks display strong signs of both core-periphery structures and temporal escalation. Furthermore, we find evidence for the effects of attribute-related mechanisms and interaction repetition in all three networks, yet not for triadic closure regardless of its theoretical prominence.
We conclude by highlighting the usefulness of RHEM for vast array of (criminal) network data that is frequently recorded as hyperevents. We also discuss potential practical implications in monitoring and dismantling corruption networks by emphasizing transparency during the periods of temporal escalation, more complex disruption targeting the cores in their entirety, and using the information about the underlying mechanisms to avoid negative unintended consequences.

Author

• Tomáš Diviák, University of Manchester