ACCDS: A Criminal Community Detection System Based on Evolving Social Graphs
This paper presents an intelligent criminal community detection system, called ACCDS, to support various criminal event detection tasks such as drug abuse behavior discovery and illegal pyramid selling organization detection, based on evolving social graphs. The system contains four main components: data collection, community social graph construction, criminal community detection and data visualization. First, the system collects a large amount of e-government data from several real communities. The raw data consist of demographic data, social relations, house visiting records, and sampled criminal records. To protect the privacy, we desensitize the real data using some data processing techniques, and extract the important features for profiling the human behaviors. Second, we use a large static social graph to model the social relations of all residents and a sequence of time-evolving graphs to model the house visiting data for each house owner. With the graph models, we formulate the criminal community detection tasks as the subgraph mining problem, and implement a subgraph detection algorithm based on frequent pattern mining. Finally, the system provides very user-friendly interfaces to visualize the detected results to the corresponding user.
KeywordsEvolving social graphs Criminal community detection Subgraph mining Data visualization
This work is supported by the National Natural Science Foundation of China under Grant No. 61702432, the Fundamental Research Funds for Central Universities of China under Grant No. 20720180070, and the International Cooperation Projects of Fujian Province in China under Grant No. 2018I0016.
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