本文選題：社會(huì)網(wǎng)絡(luò) + 影響最大化�。� 參考：《軟件學(xué)報(bào)》2017年08期

【摘要】：影響最大化旨在從給定的社會(huì)網(wǎng)絡(luò)中尋找出一組影響力最大的子集.現(xiàn)有工作大都在假設(shè)實(shí)體點(diǎn)(個(gè)人或博客等)影響關(guān)系已知的情況下,關(guān)注于分析單個(gè)實(shí)體點(diǎn)的影響力.然而在一些實(shí)際場(chǎng)景中,人們往往更關(guān)注區(qū)域或人群等這類團(tuán)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(tuán)體的選擇問題:(1)基于團(tuán)體的關(guān)聯(lián)發(fā)現(xiàn),建立了團(tuán)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(tuán)組合.在人工數(shù)據(jù)和真實(shí)數(shù)據(jù)上,實(shí)驗(yàn)驗(yàn)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(diǎn)實(shí)驗(yàn)室(武漢大學(xué));武漢大學(xué)計(jì)算機(jī)學(xué)院;武漢大學(xué)國際軟件學(xué)院;云南大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金(61232002,61502347,61202033,61572376) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(2042015kf00 38)~~
【分類號(hào)】：TP301.6

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