基于最小點覆蓋和反饋點集的社交網(wǎng)絡影響最大化算法
doi: 10.11999/IEIT160019
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1.
(北京大學信息科學技術學院 北京 100871) ②(北京林業(yè)大學理學院 北京 100083)
基金項目:
國家自然科學基金(61370193)
Minimum Vertex Covering and Feedback Vertex Set-based Algorithm for Influence Maximization in Social Network
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1.
(School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China)
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2.
(College of Science, Beijing Forestry University, Beijing 100083, China)
Funds:
The National Natural Science Foundation of China (61370193)
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摘要: 社交網(wǎng)絡中的影響最大化問題是指在特定的傳播模型下,如何尋找k個最具影響力的節(jié)點使得在該模型下社交網(wǎng)絡中被影響的節(jié)點最多,信息傳播的范圍最廣。該問題是一個優(yōu)化問題,并且已經(jīng)被證明是NP-難的??紤]到圖的最小點覆蓋和反饋點集中的頂點對圖的連通性影響較大,該文提出一種基于最小點覆蓋和反饋點集的社交網(wǎng)絡影響最大化算法(Minimum Vertex Covering and Feedback Vertex Set, MVCFVS),并給出了具體的仿真實驗和分析。實驗結果表明,與最新的算法比較,該算法得到的節(jié)點集在多種模型下都具有優(yōu)異的傳播效果,例如在獨立級聯(lián)模型和加權級聯(lián)模型中超過當前最好的算法,并且還具有更快的收斂速度。
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關鍵詞:
- 社交網(wǎng)絡 /
- 影響最大化 /
- 傳播模型 /
- 最小點覆蓋 /
- 反饋點集
Abstract: Influence maximization is an optimization issue of finding a subset of nodes under a given diffusion model, which can maximize the spread of influence. This optimization issue has been proved to be NP-hard. Leveraging the fact that vertices in minimum vertex covering and feedback vertex set are of great importance for the connectivity of a graph, a heuristic algorithm for influence maximization based on Minimum Vertex Covering and Feedback Vertex Set (MVCFVS). Extensive experiments on various diffusion models against state of the art algorithms are carried out. Specifically, the proposed algorithm performs excellent on Independent Cascade Model (ICM) and Weighted Cascade Model (WCM), which exhibits its great advantages in terms of influence range and convergent speed. -
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