Use of Graph Technology to Identify Criminal Activity Using Call Data Record

Crime is a global issue and the first move must be to control it. For a nation to experience healthy, long-term growth, it is essential. We are well aware of the challenges in identifying the criminal domains in the digital world that are constantly influenced by their misdeeds. To stay up with crimes, offenders, and their tactics, police forces across the globe pace themselves continually. The difficulty of sifting through a large amount of data on crimes and criminals has grown significantly for the police department labor force. There is a need for a system that can categorize and thoroughly investigate. Using various strategies and concepts, police personnel can identify and stop crimes in society. In this study, we propose a novel model for investigating how graph technologies might be used to analyze Call Detail Records (CDR) in order to detect potential offenders. We are charged with the arduous task of using an unsupervised data analysis approach without including any prior knowledge about the application context in the model, automatically producing appropriate information from available data. The outcomes of graph data mining are used to create profiling data, identify anomalies, and analyze user behavior