本文選題：社會網絡 + 影響最大化　； 參考：《軟件學報》2017年08期

【摘要】：影響最大化旨在從給定的社會網絡中尋找出一組影響力最大的子集.現(xiàn)有工作大都在假設實體點(個人或博客等)影響關系已知的情況下,關注于分析單個實體點的影響力.然而在一些實際場景中,人們往往更關注區(qū)域或人群等這類團體的組合影響力,如戶外廣告、電視營銷、疫情防控等.研究了影響力團體的選擇問題:(1)基于團體的關聯(lián)發(fā)現(xiàn),建立了團體傳播模型GIC(group independent cascade);(2)根據(jù)GIC模型,給出了貪心算法CGIM(cascade group influence maximization),搜索最具影響力的top-k團組合.在人工數(shù)據(jù)和真實數(shù)據(jù)上,實驗驗證了該方法的效果和效率.
[Abstract]:The aim of maximizing influence is to find a set of most influential subsets from a given social network. Most of the existing work focuses on the analysis of the impact of individual entity points under the assumption that entity points (individuals, blogs, etc.) influence relationships are known. However, in some practical situations, people tend to pay more attention to the combined influence of such groups as region or crowd, such as outdoor advertising, television marketing, epidemic prevention and control and so on. In this paper, we study the selection problem of influence groups: 1) based on the association of groups, we establish a group propagation model GIC(group independent cascade2) according to the GIC model, we give the greedy algorithm CGIM(cascade group influence maximization to search for the most influential top-k clusters. The effectiveness and efficiency of the method are verified by experiments on artificial data and real data.
【作者單位】： 軟件工程國家重點實驗室(武漢大學);武漢大學計算機學院;武漢大學國際軟件學院;云南大學信息工程學院;
【基金】：國家自然科學基金(61232002,61502347,61202033,61572376) 中央高�；究蒲袠I(yè)務費專項資金(2042015kf00 38)~~
【分類號】：TP301.6

【相似文獻】

相關碩士學位論文 前1條

1 趙釗;基于成本效益的影響最大化算法分析與設計[D];東南大學;2015年

，

本文編號：1803373