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.
Furthermore, our tool provides a system model which aims at improving the quality of the analysis of social relationships of the network through the integration of visualization and SNA-based statistical techniques, which is a relevant topic in the ongoing research in Social Network Analysis @cite_33 .
@cite_33
1983359980 2036400208 2160321046 2066459332
Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data. In this paper we introduce a new model to represent an interconnected network of networks. This model is fundamental to reason about the real organization of on-line social networks, where users belong to and interact on different networks at the same time. In addition we extend traditional SNA measures to deal with this multiplicity of networks and we apply the model to a real dataset extracted from two microblogging sites. Social network analysis (SNA) is now a commonly used tool in criminal investigations, but evidence gathering and analysis is often restricted by data privacy laws. We consider the case where multiple investigators want to collaborate, but do not yet have sufficient evidence that justifies a plaintext data exchange. This paper proposes a solution for privacy-preserving social network analysis where several investigators can collaborate without actually exchanging sensitive private information. An investigator can request data from other sites to augment his view without revealing personally identifiable data. The investigator can compute important metrics by means of a SNA on the subject while keeping the entire social network unknown him. Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space.” We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improv...