کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11002436 | 1440625 | 2018 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel ITÃ Algorithm for influence maximization in the large-scale social networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
As a key problem in the social network, Influence Maximization(IM) has received extensive study. Since it is a well-known NP-complete problem, it is a great challenge to determine the initial diffusion seed nodes especially when the size of social network increases. In this paper, we firstly introduce a new index (named Node Key Degree, NKD) to denote the significance degree of each node. A node's NKD is determined by two factors: (1) the number of its direct previous nodes, and (2) the number of its successor offsprings within a certain number of levels. Then, we propose a novel efficient ITÃ Algorithm to solve the IM problem, termed as ITÃ-IM. There are three properties and two operators in ITÃ-IM: the formers include particle's radius, particle's activeness and environmental temperature, the later ones are drift operator and fluctuate operator. During the searching process, the particles in ITÃ can cooperate with each other to effectively balance the contradictions between exploration and exploitation existing in most of meta-heuristic algorithms. In order to understand the strengths and weaknesses of ITÃ-IM, we have carried out extensive computational studies on the six real world datasets. Experimental results show that our algorithm achieves competitive results in influence spread as compared with other four state-of-the-art algorithms in the large-scale social networks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 88, November 2018, Pages 755-763
Journal: Future Generation Computer Systems - Volume 88, November 2018, Pages 755-763
نویسندگان
Yufeng Wang, Wenyong Dong, Xueshi Dong,