Salvatore Catanese

Crawling Facebook for social network analysis purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.

Detecting criminal organizations in mobile phone networks

Abstract The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as mobile phones and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first we provide a theoretical framework for the problem of detecting and characterizing criminal organizations in networks reconstructed from phone call records. Then, we introduce an expert system to support law enforcement agencies in the task of unveiling the underlying structure of criminal networks hidden in communication data. This platform allows for statistical network analysis, community detection and visual exploration of mobile phone network data. It enables forensic investigators to deeply understand hierarchies within criminal organizations, discovering members who play central role and provide connection among sub-groups. Our work concludes illustrating the adoption of our computational framework for a real-word criminal investigation.

Extraction and Analysis of Facebook Friendship Relations

Online social networks (OSNs) are a unique web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of online social networks both from the point of view of marketing and offer of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (off-line) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem). However, OSN analysis poses novel challenges both to computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations is restricted; thus, we acquired the necessary information directly from the front end of the website, in order to reconstruct a subgraph representing anonymous interconnections among a significant subset of users. We describe our ad hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first-search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms.

Forensic analysis of phone call networks

In the context of preventing and fighting crime, the analysis of mobile phone traffic, among actors of a criminal network, is helpful in order to reconstruct illegal activities on the basis of the relationships connecting those specific individuals. Thus, forensic analysts and investigators require new advanced tools and techniques which allow them to manage these data in a meaningful and efficient way. In this paper we present LogAnalysis, a tool we developed to provide visual data representation and filtering, statistical analysis features and the possibility of a temporal analysis of mobile phone activities. Its adoption may help in unveiling the structure of a criminal network and the roles and dynamics of communications among its components. Using LogAnalysis, forensic investigators could deeply understand hierarchies within criminal organizations, for e.g., discovering central members who provide connections among different sub-groups, etc. Moreover, by analyzing the temporal evolution of the contacts among individuals, or by focusing on specific time windows they could acquire additional insights on the data they are analyzing. Finally, we put into evidence how the adoption of LogAnalysis may be crucial to solve real cases, providing as example a number of case studies inspired by real forensic investigations led by one of the authors.

Network structure and resilience of Mafia syndicates

In this paper we present the results of our study of Sicilian Mafia organizations using social network analysis. The study investigates the network structure of a Mafia syndicate, describing its evolution and highlighting its plasticity to membership-targeting interventions and its resilience to disruption caused by police operations. We analyze two different datasets dealing with Mafia gangs that were built by examining different digital trails and judicial documents that span a period of ten years. The first dataset includes the phone contacts among suspected individuals, and the second captures the relationships among individuals actively involved in various criminal offenses. Our report illustrates the limits of traditional investigative methods like wiretapping. Criminals high up in the organization hierarchy do not occupy the most central positions in the criminal network, and oftentimes do not appear in the reconstructed criminal network at all. However, we also suggest possible strategies of intervention. We show that, although criminal networks (i.e., the network encoding mobsters and crime relationships) are extremely resilient to different kinds of attacks, contact networks (i.e., the network reporting suspects and reciprocated phone calls) are much more vulnerable, and their analysis can yield extremely valuable insights.

A visual tool for forensic analysis of mobile phone traffic

In this paper we present our tool LogAnalysis for forensic visual statistical analysis of mobile phone traffic. LogAnalysis graphically represents the relationships among mobile phone users with a node-link layout. Its aim is to explore the structure of a large graph, measure connectivity among users and give support to visual search and automatic identification of organizations. To do so, LogAnalysis integrates graphical representation of network elements with measures typical of Social Network Analysis (SNA) in order to help detectives or forensic analysts to systematically examine relationships. The analysis of data extracted from mobile phone traffic logs has a fundamental relevance in forensic investigations since it allows to unveil the structure of relationships among individuals suspected to be part of criminal organizations together with the role they play inside the organization itself. To this purpose, the Social Network Analysis (SNA) methods were heavily employed in order to understand the importance of relationships. Interpretation and visual exploration of graphs representing phone contacts over a given time interval may become demanding, due to the presence of numerous nodes and edges. Our main contribution is an interface that enables systematic analysis of social relationships using visual different techniques and statistical information. LogAnalysis allows a deeper and clearer understanding of criminal associations while evidencing key members inside the criminal ring, and/or those working as link among different associations

Rendering of 3D dynamic virtual environments

In this paper we present a framework for the rendering of dynamic 3D virtual environments which can be integrated in the development of videogames. It includes methods to manage sounds and particle effects, paged static geometries, the support of a physics engine and various input systems. It has been designed with a modular structure to allow future expansions. We exploited some open-source state-of-the-art components such as OGRE, PhysX, ParticleUniverse, etc.; all of them have been properly integrated to obtain peculiar physical and environmental effects. The stand-alone version of the application is fully compatible with Direct3D and OpenGL APIs and adopts OpenAL APIs to manage audio cards. Concluding, we devised a showcase demo which reproduces a dynamic 3D environment, including some particular effects: the alternation of day and night influencing the lighting of the scene, the rendering of terrain, water and vegetation, the reproduction of sounds and atmospheric agents.

A Framework for Designing 3D Virtual Environments

The process of design and development of virtual environments can be supported by tools and frameworks, to save time in technical aspects and focusing on the content. In this paper we present an academic framework which provides several levels of abstraction to ease this work. It includes state-of-the-art components we devised or integrated adopting open-source solutions in order to face specific problems. Its architecture is modular and customizable, the code is open-source.

Uncovering Criminal Behavior with Computational Tools

In this chapter we explore the opportunities brought in by advanced social network analysis techniques to study criminal behaviors and dynamics in heterogeneous communication media, along multiple dimensions including the temporal and spatial ones. To this aim, we present LogViewer, a Web framework we developed to allow network analysts to study combinations of geo-embedded and time-varying data sources like mobile phone networks and social graphs. We present some use-cases inspired by real-world criminal investigations where we used LogViewer to study criminal networks reconstructed from mobile phone and social interactions to identify criminal behaviors and uncover illicit activities.

Resilience in criminal networks

Resilience identifies the ability of criminal networks to face pressures from law enforcement agencies and rapidly reorganize after perturbations or destabilizing attacks. Apart from environmental considerations, this concept is strongly tied to the topology of criminal networks which, unlike social networks, can be configured as hierarchical, cellular (or modular), flat or, even more frequently, as a combination of them. Resilience has implications in the techniques of investigation of law enforcement agencies especially during the phases of information gathering or planning of police actions. In this paper we review concepts and methods derived from Social Network Analysis and apply them to study the resilience of a criminal network. We also examine the evolution of a criminal network operating in Sicily (Italy), before and after the counter-actions of police agencies. We also show how the resilience of the network originated and the features that enable the dynamical reshaping of the criminal networks so as to continue illegal activities.