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تشخیص همزمان زیرگراف های فشرده ناهنجار در شبکه های اجتماعی بزرگ | ||
پدافند الکترونیکی و سایبری | ||
دوره 9، شماره 2 - شماره پیاپی 34، تیر 1400، صفحه 179-194 اصل مقاله (1.21 M) | ||
نوع مقاله: مقاله پژوهشی | ||
نویسندگان | ||
ملیحه شاه حسینی1؛ امین اله مه آبادی* 2 | ||
1کارشناسی ارشد گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، دانشگاه شاهد، تهران، ایران | ||
2هیات علمی دانشکده فنی مهندسی دانشگاه شاهد | ||
تاریخ دریافت: 22 آبان 1399، تاریخ بازنگری: 20 آذر 1399، تاریخ پذیرش: 22 دی 1399 | ||
چکیده | ||
این مقاله رویکرد جدید تشخیص ناهنجاری بدون علامت براساس پردازش سیگنال های مرتبط با اطلاعات محلی ارایه می دهد که قادر به تعیین همزمان زیرگراف های فشرده ناهنجار در گراف ناشناخته نویزی شبکه های اجتماعی بزرگ است. همچنین الگوریتم جدید نمونه برداری مبتنی بر نمونه برداری فشرده جهت بازیابی ویژگی های تنک شبکه های ثابت ارایه داده که هدفش بهبود دقتِ تشخیص ناهنجاری همراه با کاهش پیچیدگیِ نمونه برداری داده ها است. نتایج آزمایشات تجربی با داده های مصنوعی و واقعی شبکه های اجتماعی در مقایسه با مهم ترین روش های علمی نشان داد که رویکرد پیشنهادی علاوه بر برخورداری از دقت تشخیص همزمان چندین زیرگراف فشرده، پیچیدگی محاسباتی را از O(n^4 √(logn )) به O(n^2) در شبکه n گره ای کاهش داده و به آسانی قابل کاربرد در شبکه های پویای پیچیده است. | ||
کلیدواژهها | ||
تشخیص ناهنجاری؛ زیرگراف های ناهنجار؛ پردازش سیگنال؛ نمونه برداری فشرده؛ نظریه گراف | ||
عنوان مقاله [English] | ||
Concurrent Detection of Compact Anomalous Subgraphs in Large Social Networks | ||
نویسندگان [English] | ||
M. Shah Hosseini1؛ A. Mahabadi2 | ||
2Computer Engineering Department, Shahed University, Tehran, Iran. Acoustic Research Center , Shahed University, Tehran, Iran. | ||
چکیده [English] | ||
This paper presents a new approach to the detection of asymptomatic anomalies based on the signal processing related to local information of graph that simultaneously detects small compact anomalous subgraphs in the unknown graphs of large social networks. It also introduces a novell sampling algorithm based on compressive sensing to retrieve the sparse properties of static networks, which aims to improve the accuracy of anomaly detection while reducing the complexity of data sampling. The results of experimental experiments with artificial random and real datasets of social networks in comparison with the state-of-the-art methods showed that the proposed approach, in addition to having the accuracy of simultaneous detection of anomalous compact subgraphs, the computational complexity reduced from O(n^4 √(logn )) to O(n^2) in the n node networks and is easily applicable in complex dynamic networks. | ||
کلیدواژهها [English] | ||
Anomaly Detection, Anomalous Subgraphs, Signal Processing, Compressive Sensing, Graph Theory | ||
مراجع | ||
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