The information visualization of networks has been a tricky task during the last decade. Visualization of features by way of merging, linking, and grouping of entity attributes is provided to criminal network investigators. It is difficult to understand such large amounts of statistical data. A number of solutions have been proposed to tackle this bulk of information. We have found 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, by examining some dynamics of criminal networks, and by making use of some novel interactive features. The current study will help to understand interesting patterns in criminal data by way of visualization. Besides our previously proposed network visualization features, we have appended five new features. These features include ‘Pie-chart feature’ which has been proposed for better ‘details on demand’ facility to the analysts. A ‘Trend analysis feature’ is proposed for visualizing the variation in different crimes over some span of time. The ‘Graphical Trend Analysis feature’ provides a graphical interface to the analysts. There is a unique ‘Encircle feature’, with the aid of which the desired clusters can be dragged away from the dense network for easy manipulation. With ‘Similar node feature’, the analysts may get summarized information regarding the activity of different nodes which are at distant apart. We have made an evaluation of our proposed visualization features 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 features provided adequate results to the users. In the second phase, the comparison of the features had been performed against some other state-of-the-art tool. These tasks were to be performed in the groups of participants. The public data set of Chicago Narcotics 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. We have demonstrated the usability of the new features with examples by employing the datasets. We have proposed a unique way of visualizing the clustering of data, with which the analyst gets a sound visualization of the data. The usability, of the proposed features, indicates that the crime analysts will get a valuable insight into the criminal networks.

Evaluation of network visualization tools in software engineering is a tricky task. There are a number of factors, contradictions, and preferences of investigative analysts that are to be kept in consideration while designing the experiment. Complexity in data, computational overhead to reach the targeted information and scarcity of a standard platform may slow down the investigation process. In this research paper, we have made the evaluation of some of the new features of our proposed framework, PEVNET, by conducting an experiment. There were twenty four participants who had 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. We have conducted an evaluation of the newly inducted features into PEVNET. These include the 'Pie-chart feature', 'Trend analysis Feature', 'Graphical trend analysis feature', and 'Encircle feature'. In the second phase, the comparison of the PEVNET had been performed against some other state-of-the-art tools. These tasks were to be performed in the groups of participants. We found that the participants of the PEVNET group performed the tasks faster, in respect to the respective features, as compared to the other techniques used in the experiment. Further, we have found that the network visualization of the PEVNET framework, based on the experimental results, had gotten satisfactory feedback from the majority of the participants. The case study of Chicago Narcotics datasets was used. We believe that by evaluating the PEVNET in this research paper, we will be able to check the effectiveness of our proposed features.

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.

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.

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.

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.

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