مقاله انگلیسی رایگان در مورد الگوریتم موازی موثر در تشخیص گره های تاثیر گذار- الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله الگوریتم موازی موثر در تشخیص گره های تاثیر گذار در شبکه های زیستی بزرگ واحد پردازش گرافیکی
عنوان انگلیسی مقاله Efficient parallel algorithm for detecting influential nodes in large biological networks on the Graphics Processing Unit
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی  ۱۳ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۷٫۰۰۷ در سال ۲۰۱۹
شاخص H_index ۹۳ در سال ۲۰۲۰
شاخص SJR ۰٫۸۳۵ در سال ۲۰۱۹
شناسه ISSN ۰۱۶۷-۷۳۹X
شاخص Quartile (چارک) Q1 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های مرتبط الگوریتم ها و محاسبات، معماری سیستم های کامپیوتری، شبکه های کامیپوتری
نوع ارائه مقاله
ژورنال
مجله / کنفرانس سیستم های کامپیوتری نسل آینده – Future Generation Computer Systems
دانشگاه China University of Geosciences, Beijing, China
کلمات کلیدی شبکه های زیستی، گره های تاثیر گذار، مرکزیت درجه، ضریب خوشه بندی، H-Index، الگوریتم موازی
کلمات کلیدی انگلیسی Biological networks, Influential nodes, Degree Centrality, Clustering Coefficient, H-Index, Parallel algorithm
شناسه دیجیتال – doi
https://doi.org/10.1016/j.future.2019.12.038
کد محصول E14138
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract

۱٫ Introduction

۲٫ Background: Metrics for detecting influential nodes in large biological networks

۳٫ Proposed parallel algorithm for detecting influential nodes in large biological networks

۴٫ Results

۵٫ Discussion

۶٫ Conclusion

Acknowledgments

References

 

بخشی از متن مقاله:

Abstract

In biological networks, some nodes are more influential than others. The most influential nodes are those whose elimination induces a network collapse, and detecting these nodes is crucial in many circumstances. However, this is a difficult task when the size of the biological networks is large. In this paper, we have designed and implemented an efficient parallel algorithm for detecting influential nodes for large biological networks by exploiting a Graphics Processing Unit (GPU). The essential concept behind the proposed parallel algorithm is that several computationally expensive procedures in detecting influential nodes are redesigned and transformed into quite efficient GPUaccelerated primitives such as parallel sort, scan, and reduction. Four local metrics, including the Degree Centrality (DC), Companion Behavior (CB), Clustering Coefficient (CC), and H-Index, are used to measure the nodal influence. To evaluate the efficiency of the proposed parallel algorithm, five large real biological networks are employed in the experiments. The experimental results show that (1) the proposed parallel algorithm can achieve speedups of approximately 48∼۹۴ over the corresponding serial algorithm; (2) compared to a baseline parallel algorithm developed on a multi-core CPU, the proposed parallel algorithm yields speedups of 5∼۹ for DC and H-Index, while it is slightly slower for CB and CC due to the uneven degree distribution; and (3) when using DC and H-Index, the proposed parallel algorithm is capable of detecting the influential nodes in a large biological network consisting of 150 million edges in less than 3 s.

Introduction

In recent years, complex network analysis has received increasing attention. Many real complex systems can be abstractly regarded as complex networks for presenting the complexities of real systems [1], such as social networks, technological networks, information networks, and biological networks. Various methods have been proposed for mining information from complex networks. In particular, detecting the influential nodes in complex networks is a topic of interest drawing much attention in this research field [2–۴].

Detecting influential nodes in complex networks can be exploited to mine the features and functions of these networks [2]. Much research has been conducted to rank and identify influential nodes in the aforementioned four network categories. In the detection of influential nodes in various networks, the first critical issue is to select or define the metrics for measuring the influence of each node; the second is to employ or develop specific algorithms to effectively and efficiently determine and rank the influential nodes.

Many metrics have been proposed for detecting the influential nodes in complex networks. These metrics can be roughly divided into two categories [4]: (1) local metrics that are calculated based on the local structures of networks and (2) global metrics that are calculated based on the global structures of networks. For the first category, the most commonly used metrics include the degree centrality, clustering coefficient, and H-Index [5]. For the second category, the most commonly used metrics are the betweenness centrality [6], closeness centrality, PageRank, k-core [7] computed using the k-shell decomposition [8], and the bidirectional k-core (B-core) [9].

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