Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
Fan Lu, Quan Qi, Huaibin Qin
TL;DR
The paper tackles real-time anomaly detection and information completion in social network knowledge graphs within edge computing constraints. It introduces a lightweight distributed KG completion pipeline with a PDQA data quality assessor and a gradient-based model pruning strategy, evaluating 11 KG embedding models to identify a strong baseline. RotatE emerges as the top performer, achieving substantial completion accuracy while the pruning process reduces model size by 70% and enables edge deployment with minimal accuracy loss (Hits@10 ~ 86.97%). The approach demonstrates practical impact for public safety by delivering fast, privacy-preserving reasoning on edge devices, validated on a large personnel-relations knowledge graph. Future work aims to broaden datasets and uncover latent relations through richer semantic representations.
Abstract
In the rapidly advancing information era, various human behaviors are being precisely recorded in the form of data, including identity information, criminal records, and communication data. Law enforcement agencies can effectively maintain social security and precisely combat criminal activities by analyzing the aforementioned data. In comparison to traditional data analysis methods, deep learning models, relying on the robust computational power in cloud centers, exhibit higher accuracy in extracting data features and inferring data. However, within the architecture of cloud centers, the transmission of data from end devices introduces significant latency, hindering real-time inference of data. Furthermore, low-latency edge computing architectures face limitations in direct deployment due to relatively weak computing and storage capacities of nodes. To address these challenges, a lightweight distributed knowledge graph completion architecture is proposed. Firstly, we introduce a lightweight distributed knowledge graph completion architecture that utilizes knowledge graph embedding for data analysis. Subsequently, to filter out substandard data, a personnel data quality assessment method named PDQA is proposed. Lastly, we present a model pruning algorithm that significantly reduces the model size while maximizing performance, enabling lightweight deployment. In experiments, we compare the effects of 11 advanced models on completing the knowledge graph of public security personnel information. The results indicate that the RotatE model outperforms other models significantly in knowledge graph completion, with the pruned model size reduced by 70\%, and hits@10 reaching 86.97\%.}