Using the Spanning Tree of a Criminal Network for Identifying Its Leaders

Abstract

We introduce a forensic analysis system called ECLfinder that identifies the influential members of a criminal organization as well as the immediate leaders of a given list of lower-level criminals. Criminal investigators usually seek to identify the influential members of criminal organizations, because eliminating them is most likely to hinder and disrupt the operations of these organizations and put them out of business. First, ECLfinder constructs a network representing a criminal organization from either Mobile Communication Data associated with the organization or crime incident reports that include information about the organization. It then constructs a Minimum Spanning Tree (MST) of the network. It identifies the influential members of a criminal organization by determining the important vertices in the network representing the organization, using the concept of existence dependency. Each vertex v is assigned a score, which is the number of other vertices, whose existence in MST is dependent on v. Vertices are ranked based on their scores. Criminals represented by the top ranked vertices are considered the influential members of the criminal organization represented by the network. We evaluated the quality of ECLfinder by comparing it experimentally with three other systems. Results showed marked improvement.

Identification of Influencers in Social Media using Social Network Analysis (SNA)

“…In addition, the ECLfinder is combined with the Minimum Spanning Tree and SNA measurements (Degree Centrality, Closeness Centrality, and Betweenness Centrality) to find the influencers. The results show some important vertices that act as influencers and influencer leader [9].…”

“…This research will focus on the measurement of Degree Centrality, Closeness Centrality, and Betweenness Centrality. These measurements have been used by previous studies with various combination of methods for finding influencers [1,4,9]. Each centrality measurement result will be combined with weighting to each of the centrality values in each vertex.…”

Supervised machine learning for classification mining and ranking of illegal web contents = Aprendizaje automático supervisado para la clasificación, extracción de conocimiento y ordenación de contenidos web ilegales

“…Then, they assessed the performance of the weighted HITS (Kleinberg, 1999) and PageRank (Page et al, 1999). Taha and Yoo (2017) presented an algorithm using the Minimum Spanning Tree (MST) to build a network of criminals. Each node was assigned a score that was proportional to the number of nodes whose existence depend on the existence of the targeted node.…”

“…They proposed a score that captures the interaction between users and their followers, regarding all the published tweets. Taha and Yoo (2017) employed the existence dependency concept on a network of criminals.…”

“…Fronzetti Colladon and Remondi (2017) exploró el uso de métricas de las redes sociales, como la centralidad interna, externa y de proximidad para combatir los delitos de lavado de dinero. Taha and Yoo (2017) presentó un algoritmo que utiliza el Árbol de Expansión Mínima (MST, del inglés Minimum Spanning Tree) para construir una red de delincuentes. A cada nodo se le asignó una puntuación proporcional al número de nodos cuya existencia depende de la existencia del nodo objetivo.…”