Developing a Model for Explaining Network Attributes and Relationships of Organised Crime Activities by Utilizing Network Science

The main objective of this research is to provide an innovative exploratory model for investigating substantive organised crime activities. The study articulates 30 critical independent variables related to organised crime, network science and a comprehensive exploratory approach which converts measurements of the variables into meaningful crime related inferences and conclusions. A case study was conducted to review initial feasibility of the selected variables, exploratory approach and model, and the results suggesting good effectiveness and useability

Structure and dynamics of core-periphery networks

Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways ensuring cooperation and providing multiple options of network flow re-channelling greatly contribute to the high robustness of complex systems. Core processes enable a coordinated response to various stimuli, decrease noise, and evolve slowly. The integrative function of network cores is an important step in the development of a large variety of complex organisms and organizations. In addition to these important features and several decades of research interest, studies on core/periphery networks still have a number of unexplored areas.Comment: a comprehensive review of 41 pages, 2 figures, 1 table and 182 reference

Greedy methods for approximate graph matching with applications for social network analysis

In this thesis, we study greedy algorithms for approximate sub-graph matching with attributed graphs. Such algorithms find one or multiple copies of a sub-graph pattern from a bigger data graph through approximate matching. One intended application of sub-graph matching method is in Social Network Analysis for detecting potential terrorist groups from known terrorist activity patterns. We propose a new method for approximate sub-graph matching which utilizes degree information to reduce the search space within the incremental greedy search framework. In addition, we have introduced the notion of a “seed” in incremental greedy method that aims to find a good initial partial match. Simulated data based on terrorist profiles database is used in our experiments that compare the computational efficiency and matching accuracy of various methods. The experiment results suggest that with increasing size of the data graph, the efficiency advantage of degree-based method becomes more significant, while degree-based method remains as accurate as incremental greedy. Using a “seed” significantly improves matching accuracy (at the cost of decreased efficiency) when the attribute values in the graphs are deceptively noisy. We have also investigated a method that allows to expand a matched sub-graph from the data graph to include those nodes strongly connected to the current match

Detecting Covert Networks in Multilingual Groups: Evidence within a Virtual World

This paper introduces an approach for the examination and organization of unstructured text to identify relationships between networks of individuals. This approach uses discourse analysis to identify information providers and recipients and determines the structure of covert organizations irrespective of the language that facilitate conversations between members. Then, this method applies social network analytics to determine the arrangement of a covert organization without any a priori knowledge of the network structure. This approach is tested and validated using communication data collected in a virtual world setting. Our analysis indicates that the proposed framework successfully detected the covert structure of three information networks, and their cliques, within an online gaming community during a simulation of a large-scale event

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum