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Chiara Broccatelli

Thesis title - ‘Going Beyond Secrecy: Methodological Advances for Two-mode Temporal Criminal Networks with Social Network Analysis’.

(Passed 2017)

The contexts where covert networks operate are varied wherefore individuals can be involved in several kinds of activities such as drug-trafficking, the Mafia, terrorism and various covert social movements. Starting from the evidence that individuals whom perform covert activities tend to act together and strengthen the cohesiveness of a network, it can be noticed that the aspect of collaboration plays a crucial role in this context. For this reason, my research mainly focuses on the aspect of collaboration and co-working between individuals operating in secrecy over time.


This thesis seeks to extend the application of Social Network Analysis (SNA) to temporal graphs, in particular providing new insights for the understanding of covert networks. The analyses undertaken reveal informative features and properties of individuals’ affiliations under covertness that also illustrate how both individuals and events influence the network structure.

The review of the literature on covert networks provided in the initial two chapters suggests the presence of some ambiguities concerning how authors define structural properties and dynamics of covert networks. Authors sometimes disagree and use their findings to explain opposite views about covert networks. The controversy in the field is used as a starting point in order to justify the methodological application of SNA to understand how individuals involved in criminal and illegal activities interact with each other.

I attempt to use a deductive approach, without preconceived notions about covert network characteristics. In particular, I avoid considering covert networks as organisations in themselves or as cohesive groups. I focus on individuals and their linkages constructed from their common participation in illicit events such as secret meetings, bombing attacks and criminal operations.

In order to tackle these processes I developed innovative methods for investigating criminals’ behaviours over time and their willingness to exchange tacit information. The strategy implies the formulation of a network model in order to represent and incorporate in a graph three types of information: individuals, events, and the temporal dimension of events. The inclusion of the temporal dimension offers the possibility of adopting a more comprehensive theoretical framework for considering individuals and event affiliations. This thesis expands the analysis of bipartite covert networks by adopting several avenues to explore in this perspective.

Chapter 3 proposes a different way to represent two-mode networks starting from the use of line-graphs, namely the bi-dynamic line-graph data representation (BDLG), through which it is possible to represent the temporal evolution of individual’s trajectories.

The following chapter 4 presents some reflections about the idea of cohesion and cohesive subgroups specific to the case of two-mode networks. Based on the affiliation matrices, the analysis of local clustering through bi-cliques offers an attempt to analyse the mechanism of selecting accomplices while taking into account time.

Chapter 5 is concerned with the concept of centrality of individuals involved in flows of knowledge exchanges. The theoretical and analytical framework helps in elaborating how individuals share their acquired hands-on experiences with others by attending joint task activities over time.

Chapter 6 provides an application of the approaches introduced in the preceding chapters to the specific case of the Noordin Top terrorist network. Here, the knowledge of experience flow centrality measure opens up a new way to quantify the transmission of information and investigate the formation of the criminal capital.

Finally, the last Chapter 7 presents some future research extensions by illustrating the versatility of the proposed approaches in order to provide new insights for the understanding of criminals’ behaviours.


  • Prof Martin Everett
  • Dr Johan Koskinen

Research interests

Social Network Analysis, Social Statistics, Covert Social Networks, Collective Action, Time Dynamics


BSc Sociology, The University of Trento

MSc Sociology and Social Research, The University of Trento

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