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

[1]  Luciano Rossoni,et al.  Models and methods in social network analysis , 2006 .

[2]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[4]  Philippe Castagliola,et al.  A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations , 2004, IEEE Symposium on Information Visualization.

[5]  Linton C. Freeman,et al.  Carnegie Mellon: Journal of Social Structure: Visualizing Social Networks Visualizing Social Networks , 2022 .

[6]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[7]  Marti A. Hearst,et al.  Animated exploration of dynamic graphs with radial layout , 2001, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001..

[8]  Piet Hut,et al.  A hierarchical O(N log N) force-calculation algorithm , 1986, Nature.

[9]  Ben Shneiderman,et al.  Integrating statistics and visualization: case studies of gaining clarity during exploratory data analysis , 2008, CHI.

[10]  John Scott Social Network Analysis , 1988 .

[11]  Eytan Adar,et al.  GUESS: a language and interface for graph exploration , 2006, CHI.

[12]  Frank Harary,et al.  Graph theory in network analysis , 1983 .

[13]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Vladimir Batagelj,et al.  Exploratory Social Network Analysis with Pajek , 2005 .

[15]  Danah Boyd,et al.  Vizster: visualizing online social networks , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[16]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[17]  Jeffrey Heer,et al.  prefuse: a toolkit for interactive information visualization , 2005, CHI.

[18]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[19]  Ben Shneiderman,et al.  Balancing Systematic and Flexible Exploration of Social Networks , 2006, IEEE Transactions on Visualization and Computer Graphics.

[20]  Padhraic Smyth,et al.  Analysis and Visualization of Network Data using JUNG , 2005 .