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In the rapidly advancing information era, various human behaviors are beingprecisely recorded in the form of data, including identity information,criminal records, and communication data. Law enforcement agencies caneffectively maintain social security and precisely combat criminal activitiesby analyzing the aforementioned data. In comparison to traditional dataanalysis methods, deep learning models, relying on the robust computationalpower in cloud centers, exhibit higher accuracy in extracting data features andinferring data. However, within the architecture of cloud centers, thetransmission of data from end devices introduces significant latency, hinderingreal-time inference of data. Furthermore, low-latency edge computingarchitectures face limitations in direct deployment due to relatively weakcomputing and storage capacities of nodes. To address these challenges, alightweight distributed knowledge graph completion architecture is proposed.Firstly, we introduce a lightweight distributed knowledge graph completionarchitecture that utilizes knowledge graph embedding for data analysis.Subsequently, to filter out substandard data, a personnel data qualityassessment method named PDQA is proposed. Lastly, we present a model pruningalgorithm that significantly reduces the model size while maximizingperformance, enabling lightweight deployment. In experiments, we compare theeffects of 11 advanced models on completing the knowledge graph of publicsecurity personnel information. The results indicate that the RotatE modeloutperforms other models significantly in knowledge graph completion, with thepruned model size reduced by 70\%, and hits@10 reaching 86.97\%.}

Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs