Identifying a Criminal's Network of Trust

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (Presented at Third International Workshop on Complex Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27, November 2014

Early Identification of Violent Criminal Gang Members

Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by law-enforcement to the problem in the case where the network is discovered overtime by virtue of new arrests - mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.Comment: SIGKDD 201

Graph Theory Applications in Advanced Geospatial Research

Geospatial sciences include a wide range of applications, from environmental monitoring transportation to infrastructure planning, as well as location-based analysis and services. Graph theory algorithms in mathematics have emerged as indispensable tools in these domains due to their capability to model and analyse spatial relationships efficiently. This article explores the applications of graph theory algorithms in geospatial sciences, highlighting their role in network analysis, spatial connectivity, geographic information systems, and various other spatial problem-solving scenarios like digital twin. The article provides a comprehensive idea about graph theory's key concepts and algorithms that assist the geospatial modelling processes and insights into real-world geospatial challenges and opportunities. It lists the extensive research, innovative technologies and methodologies implemented in this domain

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

Complex network tools to enable identification of a criminal community

Retrieving criminal ties and mining evidence from an organised crime incident, for example money laundering, has been a difficult task for crime investigators due to the involvement of different groups of people and their complex relationships. Extracting the criminal association from enormous amount of raw data and representing them explicitly is tedious and time consuming. A study of the complex networks literature reveals that graph-based detection methods have not, as yet, been used for money laundering detection. In this research, I explore the use of complex network analysis to identify the money laundering criminals’ communication associations, that is, the important people who communicate between known criminals and the reliance of the known criminals on the other individuals in a communication path. For this purpose, I use the publicly available Enron email database that happens to contain the communications of 10 criminals who were convicted of a money laundering crime. I show that my new shortest paths network search algorithm (SPNSA) combining shortest paths and network centrality measures is better able to isolate and identify criminals’ connections when compared with existing community detection algorithms and k-neighbourhood detection. The SPNSA is validated using three different investigative scenarios and in each scenario, the criminal network graphs formed are small and sparse hence suitable for further investigation. My research starts with isolating emails with ‘BCC’ recipients with a minimum of two recipients bcc-ed. ‘BCC’ recipients are inherently secretive and the email connections imply a trust relationship between sender and ‘BCC’ recipients. There are no studies on the usage of only those emails that have ‘BCC’ recipients to form a trust network, which leads me to analyse the ‘BCC’ email group separately. SPNSA is able to identify the group of criminals and their active intermediaries in this ‘BCC’ trust network. Corroborating this information with published information about the crimes that led to the collapse of Enron yields the discovery of persons of interest that were hidden between criminals, and could have contributed to the money laundering activity. For validation, larger email datasets that comprise of all ‘BCC’ and ‘TO/CC’ email transactions are used. On comparison with existing community detection algorithms, SPNSA is found to perform much better with regards to isolating the sub-networks that contain criminals. I have adapted the betweenness centrality measure to develop a reliance measure. This measure calculates the reliance of a criminal on an intermediate node and ranks the importance level of each intermediate node based on this reliability value. Both SPNSA and the reliance measure could be used as primary investigation tools to investigate connections between criminals in a complex network