Complex networks as the World Wide Web, the web of human sexual contacts or criminal networks often do not have an engineered architecture but instead are self-organized by the actions of a large number of individuals. From these local interactions non-trivial global phenomena can emerge as small-world properties or scale-free degree distributions. A simple model for the evolution of acquaintance networks highlights the essential dynamical ingredients necessary to obtain such complex network structures. The model generates highly clustered networks with small average path lengths and scale-free as well as exponential degree distributions. It compares well with experimental data of social networks, as for example coauthorship networks in high energy physics.

Dynamics of social networks

This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-thrtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The authors would like to thank Colombian National Police and its Office of Telematics for their support on development of this project.

A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

The Police as law enforcers who authorize in terms of social protection are expected to do both the prevention and investigation efforts also the settlement of criminal cases that occurred in the society. This research can help police to identify the main actor faster and leads to solving crime-cases. The use of overall centrality is very helpful in determining the main actors from other centrality measures. The purpose of this research is to identify the central actor of crimes done by several people. Semantic Social Network Analysis is used to perform central actor identification using five centrality measurements, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and overall centrality. As for the relationship between actors, this research used social relation such as friendship, colleague, family, date or lover, and acquaintances. The relationship between actors is measured by first four centrality measures then accumulated by overall centrality to determine the main actor. The result showed 80.39% accuracy from 102 criminal cases collected with at least 3 actors involved in each case.

/pdf/central-actor-identification-of-crime-group-using-semantic-469a0bvg52.pdf

Central Actor Identification of Crime Group using Semantic Social Network Analysis

Community-level event (CLE) datasets, such as police reports of crime events, contain abundant semantic information of event situations, and descriptions in a geospatial-temporal context. They are critical for frontline users, such as police officers and social workers, to discover and examine insights about community neighborhoods. We propose CLEVis, a neighborhood visual analytics system for CLE datasets, to help frontline users explore events for insights at community regions of interest, namely fine-grained geographical resolutions, such as small neighborhoods around local restaurants, churches, and schools. CLEVis fully utilizes semantic information by integrating automatic algorithms and interactive visualizations. The design and development of CLEVis are conducted with solid collaborations with real-world community workers and social scientists. Case studies and user feedback are presented with real-world datasets and applications.

/pdf/clevis-a-semantic-driven-visual-analytics-system-for-jiht9z4dxb.pdf

CLEVis: A Semantic Driven Visual Analytics System for Community Level Events

Analyzing complexities in criminal networks is a complex issue. They become even worse when there is involvement of external collaborative networks. Criminal nodes in different criminal sub-groups combine together to form a big network. It is difficult to explore criminal activity that is building up among the sub-groups. Data from the initial investigations reveal only partial information. Hence, there is a need to find links between the data for getting adequate information. We have introduced novel visualization features that can help trace the collaborations of the individual criminal nodes with other nodes and detect the patterns of hidden criminal activities in the sub-clusters. The current study demonstrates our proposed visualization tool by using a case study of the Chicago narcotics datasets. The PEVNET tool can support crime analysts in analyzing the intra-network criminal activities. Our novel features will not only help the crime analysts in building a rationale but also in strengthening their viewpoints using PEVNET.

/pdf/a-tool-for-analysis-and-visualization-of-criminal-networks-9ve2ahkpvh.pdf

A Tool for Analysis and Visualization of Criminal Networks

No major criminal activity is possible without a comprehensive plot behind it. Detecting and understanding criminal activity has been a challenging task for the researchers in criminal networks. One important way of addressing those challenges has been visualization of criminal networks. We propose a framework called PEVNET in which existing visualization techniques for criminal networks are re-designed from a different perspective. Visualization features by way of merging, linking, and grouping of entity attributes is provided to criminal network investigators. Furthermore, we believe that the prevailing challenges to information visualization can be eliminated to a large extent by detecting evolving network patterns, which are extracted by way of visual analysis of criminal activity based on temporal data. Finally, the proposed framework will indicate the most central person in the network in a unique way, which will support the investigators' decision making.

PEVNET: a framework for visualization of criminal networks

Information visualization has been a burning topic among the researchers in the recent decade. Getting targeted information, which is everyone’s desire, is becoming difficult with the abundance of data. In this research, we have made an evaluation of our proposed framework PEVNET by conducting an experiment. Thirty two participants evaluated the system. The experiment was performed in two phases. In the first phase, a usability evaluation and qualitative feedback was carried out to check whether the PEVNET framework provided adequate results to the users. The qualitative feedback was performed by considering two aspects: the ease of use and the functionality. In the second phase, the comparison of the PEVNET had been performed against another state-of-the-art tool. Locating the central person, detecting the hidden interaction patterns between the sub-clusters, and detecting temporal activity were among the main tasks that were to be achieved by the participants. These tasks were to be performed in the groups of participants. The case study of Chicago Narcotics datasets was used. We found that the participants, of the PEVNET group, performed the tasks faster as compared to the other techniques used in the experiment. Among the participants, there were a few domain experts who appreciated our novel visualization features. Anecdotally, we believe that by evaluating the PEVNET in this research paper, we will be able to get the confidence of the crime analysts. We have found that the network visualization of the PEVNET framework, based on the experimental results, has gotten satisfactory feedback from the majority of the participants.

Evaluating PEVNET: A framework for visualization of criminal networks

The information visualization of networks has been a tricky task during the last decade. It is difficult to understand such large amounts of statistical data. A number of solutions have been proposed to tackle this bulk of information. By examining some dynamics of criminal networks and by making use of some novel interactive features, we have found that the prevailing challenges to information visualization can be eliminated to a large extent. The current study will help understand interesting patterns, which are extracted by way of monitoring the temporal data of a criminal activity. We have appended six more features to the PEVNET framework. These are ‘Node color feature’, ‘Link size feature’, ‘Link details on demand feature’, ‘Detecting collaborating sub-cluster feature’, ‘Sub-cluster detection feature’, and ‘Temporal pattern feature’. A novel clustering algorithm has been proposed. We have proposed a unique way of visualizing the clustering of data, with which the analyst gets a sound visualization of the data.

Novel Visualization Features of Temporal Data Using PEVNET

Novel Analysis and Visualization Features in PEVNET

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