Network Features in Complex Applications

Thesis


Cavallaro, Lucia 2021. Network Features in Complex Applications. Thesis
AuthorsCavallaro, Lucia
Qualification namePhD
Abstract

The aim of this thesis is to show the potential of Graph Theory and Network Science applied in real-case scenarios. Indeed, there is a gap in the state-of-art in combining mathematical theory with more practical applications such as helping the Law Enforcement Agencies (LEAs) to conduct their investigations, or in Deep Learning techniques which enable Artificial Neural Networks (ANNs) to work more efficiently. In particular, three main case studies on which evaluate the goodness of Social Network Analysis (SNA) tools were considered: (i) Criminal Networks Analysis, (ii) Networks Resilience, and (iii) ANN topology.

We have addressed two typical problems in dealing with criminal networks: (i) how to efficiently slow down the information spreading within the criminal organisation by prompt and targeted investigative operations from LEAs and (ii) what is the impact of missing data during LEAs investigation.

In the first case, we identified the appropriate centrality metric to effectively identify the criminals to be arrested, showing how, by neutralising only 5% of the top-ranking affiliates, the network connectivity dropped by 70%.

In the second case, we simulated the missing data problem by pruning some criminal networks by removing nodes or links and compared these networks against the originals considering four metrics to compute graph similarities. We discovered that a negligible error (i.e., 30% difference from the real network) was detected when, for example, some wiretaps are missing. On the other hand, it is crucial to investigate the suspects in a timely fashion, since any exclusion of suspects from an investigation may lead to significant errors (i.e., 80% difference).

Next, we defined a new approach for simulating network resilience by a probabilistic failure model. Indeed, while the classical approach for removing nodes was always successful, such an assumption was not realistic. Thus, we defined some models simulating the scenario in which nodes oppose resistance against removal. Once identified the centrality metric that on average, generates the biggest damage in the connectivity of the networks under scrutiny, we have compared our outcomes against the classical node removal approach, by ranking the nodes according to the same centrality metric, which confirmed our intuition.

Lastly, we adopted SNA techniques to analyse ANNs. In particular, we moved a step forward from earlier works because not only did our experiments confirm the efficiency arising from training sparse ANNs, but they also managed to further exploit sparsity through a better tuned algorithm, featuring increased speed at a negligible accuracy loss. We focused on the role of the parameter used to fine-tune the training phase of Sparse ANNs. Our intuition has been that this step can be avoided as the accuracy loss is negligible and, as a consequence, the execution time is significantly reduced. Yet, it is evident that Network Science algorithms, by keeping sparsity in ANNs, are a promising direction for accelerating their training processes.

All these studies pave the way for a range of unexplored possibilities for an effective use of Network Science at the service of society.

KeywordsNetwork Science; Graph Theory; Criminal Networks; Artificial Intelligence
Year2021
PublisherUniversity of Derby
Web address (URL)http://hdl.handle.net/10545/626232
http://creativecommons.org/publicdomain/zero/1.0/
hdl:10545/626232
File
File Access Level
Open
File
File Access Level
Open
File
File Access Level
Open
Publication process dates
Deposited21 Jan 2022, 16:40
Publication dates20 Dec 2021
Rights

CC0 1.0 Universal

ContributorsBagdasar, Ovidiu (Advisor), Kurugollu, Fatih (Advisor) and Liotta, Antonio (Advisor)
Permalink -

https://repository.derby.ac.uk/item/9242w/network-features-in-complex-applications

Download files


File
license.txt
File access level: Open

license_rdf
File access level: Open

Lucia_Cavallaro_PhD_Thesis.pdf
File access level: Open

  • 26
    total views
  • 8
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks
Cavallaro, Lucia, Grassia, Marco, Fiumara, Giacomo, Mangioni, Giuseppe, De Meo, Pasquale, Carchiolo, Vincenza, Bagdasar, Ovidiu and Liotta, Antonio 2022. Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks. in: Complex Networks & Their Applications X Springer.
Social network analysis: the use of graph distances to compare artificial and criminal networks
Ficara, Annamaria, Curreri, Francesco, Cavallaro, Lucia, De Meo, Pasquale, Fiumara, Giacomo, Bagdasar, Ovidiu and Liotta, Antonio 2021. Social network analysis: the use of graph distances to compare artificial and criminal networks. Journal of Smart Environments and Green Computing. https://doi.org/10.20517/jsegc.2021.08
Criminal networks analysis in missing data scenarios through graph distances
Ficara, Annamaria, Cavallaro, Lucia, Curreri, Francesco, Fiumara, Giacomo, De Meo, Pasquale, Bagdasar, Ovidiu, Song, Wei and Liotta, Antonio 2021. Criminal networks analysis in missing data scenarios through graph distances. PLos ONE. 16 (8), p. e0255067. https://doi.org/10.1371/journal.pone.0255067
Graph and Network Theory for the Analysis of Criminal Networks
Cavallaro, Lucia, Bagdasar, Ovidiu, De Meo, Pasquale, Fumara, Giacomo and Liotta, Antonio 2021. Graph and Network Theory for the Analysis of Criminal Networks. in: Springer, Cham.
Artificial neural networks training acceleration through network science strategies
Cavallaro, Lucia, Bagdasar, Ovidiu, De Meo, Pasquale, Fiumara, Giacomo and Liotta, Antonio 2020. Artificial neural networks training acceleration through network science strategies. Soft Computing. https://doi.org/10.1007/s00500-020-05302-y
Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia
Cavallaro, Lucia, Ficara, Annamaria, De Meo, Pasquale, Fiumara, Giacomo, Catanese, Salvatore, Bagdasar, Ovidiu, Song, Wei and Liotta, Antonio 2020. Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia. PLos ONE. 15 (8), p. e0236476. https://doi.org/10.1371/journal.pone.0236476