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基于三級鄰居的復(fù)雜網(wǎng)絡(luò)節(jié)點(diǎn)影響力度量方法

楊書新,  梁文,  朱凱麗

楊書新, 梁文, 朱凱麗. 基于三級鄰居的復(fù)雜網(wǎng)絡(luò)節(jié)點(diǎn)影響力度量方法[J]. 電子與信息學(xué)報(bào), 2020, 42(5): 1140-1148. doi: 10.11999/JEIT190440
引用本文: 楊書新, 梁文, 朱凱麗. 基于三級鄰居的復(fù)雜網(wǎng)絡(luò)節(jié)點(diǎn)影響力度量方法[J]. 電子與信息學(xué)報(bào), 2020, 42(5): 1140-1148. doi: 10.11999/JEIT190440
Shuxin YANG, Wen LIANG, Kaili ZHU. Measurement of Node Influence Based on Three-level Neighbor in Complex Networks[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1140-1148. doi: 10.11999/JEIT190440
Citation: Shuxin YANG, Wen LIANG, Kaili ZHU. Measurement of Node Influence Based on Three-level Neighbor in Complex Networks[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1140-1148. doi: 10.11999/JEIT190440

基于三級鄰居的復(fù)雜網(wǎng)絡(luò)節(jié)點(diǎn)影響力度量方法

doi: 10.11999/JEIT190440 cstr: 32379.14.JEIT190440

  • 江西理工大學(xué)信息工程學(xué)院 贛州 341000

基金項(xiàng)目: 國家自然科學(xué)基金(61662028)，江西省教育廳科學(xué)技術(shù)研究項(xiàng)目基金(GJJ170518)，江西省研究生創(chuàng)新專項(xiàng)資金項(xiàng)目(YC2018-S331)
詳細(xì)信息
    作者簡介:

    楊書新：男，1979年生，副教授，研究方向?yàn)樯鐣W(wǎng)絡(luò)分析、生物信息學(xué)

    梁文：男，1994年生，碩士生，研究方向?yàn)閺?fù)雜網(wǎng)絡(luò)、計(jì)算傳播學(xué)

    朱凱麗：女，1994年生，碩士生，研究方向?yàn)殡[私保護(hù)、推薦系統(tǒng)

    通訊作者:

    楊書新　 yimuyunlang@sina.com

  • 中圖分類號: TP39

Measurement of Node Influence Based on Three-level Neighbor in Complex Networks

  • Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China

Funds: The National Natural Science Foundation of China (61662028), The Scientific Technology Research Foundation of the Education Department of Jiangxi Province (GJJ170518), The Special Foundation of Postgraduate Innovation of Jiangxi province (YC2018-S331)
  • 摘要:

    已有的節(jié)點(diǎn)影響力度量方法均存在一定的局限性。該文基于三度影響力原則，綜合考慮局部度量的適宜層次及大規(guī)模網(wǎng)絡(luò)的可擴(kuò)展性，提出一種基于3級鄰居的節(jié)點(diǎn)影響力度量方法(TIM)。該方法將節(jié)點(diǎn)2, 3級具有傳播衰減特性的鄰居視為整體，用于度量節(jié)點(diǎn)的影響能力。利用傳染病模型及獨(dú)立級聯(lián)模型，在3個真實(shí)數(shù)據(jù)集驗(yàn)證了該方法的有效性。實(shí)驗(yàn)結(jié)果表明，基于3級鄰居的節(jié)點(diǎn)影響力度量方法在影響力一致性、區(qū)分度、排序性等指標(biāo)中表現(xiàn)優(yōu)越，且能夠有效求解影響力最大化問題。

    Abstract:

    There are some limitations in the existing metric methods for measuring node influence. A measurement method of node influence with three-level neighbors is proposed, which is based on the principle of three-degree influence, and considering the appropriate level of local measurement and the scalability of the large-scale network. Firstly, the neighbors with propagation attenuation characteristics in the second and third level of a node are regarded as a whole, which is used to measure the influence of the node. Then, an algorithm for measure called Three-level Influence Measurement (TIM) is proposed. Finally, in order to validate the effectiveness of the algorithm, the experiments on three datasets are conducted by using susceptible-infected-recovered model and independent cascade model. The experimental results show that the proposed algorithm is superior in consistency of influence, discrimination, sorting performance and other evaluation indexes. Furthermore, the TIM is applied to effectively solve the problem of maximizing influence.

    • HTML全文

  • 圖  1  3級影響傳播示例

    圖  2  參數(shù) $R\left( \theta \right)$ 對應(yīng)的直方圖

    圖  3  影響力一致性實(shí)驗(yàn)結(jié)果

    圖  4  排序性能

    圖  5  p2p-Gnutella08數(shù)據(jù)集實(shí)驗(yàn)結(jié)果

    圖  6  CA-HepTH數(shù)據(jù)集實(shí)驗(yàn)結(jié)果

    圖  7  WiKi-Vote數(shù)據(jù)集

    　算法1 TIM度量方法
    　輸入: G=(V, E, P) /*P 表示傳播概率*/
    　輸出: 每個節(jié)點(diǎn)的TIM度量值
    　(1) function: F(·) /*1級鄰居的層序遍歷函數(shù)*/
    　(2) for each u in V do：
    　(3) TIM(u) = 0, x=0, l=0 /*l 為集合的長度*/
    　(4) for each v in F(u) do:
    　(5) x += p(u, v)
    　(6) end for
    　(7) TIM(u) = $\theta \cdot $ exp(x)
    　(8) for each v in F(u) do:
    　(9) for each w in F(v) \{u} do:
    　(10) l=getSize ( {F(w) , w } \{ F(u)})
    　(11) TIM(u) += p(u, v) × p(v, w) × l
    　(12) end for
    　(13) end for
    　(14) end for
    下載: 導(dǎo)出CSV

    表  1  網(wǎng)絡(luò)數(shù)據(jù)集基本特征

    p2p-Gnutella08 CA-HepTh WiKi-Vote
    節(jié)點(diǎn)數(shù) 6301 9877 7115
    邊數(shù) 20777 51971 100762
    平均度 6.595 5.264 28.324
    聚類系數(shù) 0.015 0.600 0.209
    下載: 導(dǎo)出CSV

    表  2  精度提高比(%)

    p2p-Gnutella08 Wiki-Vote CA-HepTh
    Top-10 10.00 133.33 36.00
    Top-20 22.58 280.00 16.46
    Top-30 15.87 278.69 5.41
    Top-40 2.20 291.60 16.09
    Top50 7.56 272.59 3.32
    Top-60 15.61 286.55 11.71
    Top-70 13.77 263.78 10.25
    Top-80 9.44 323.90 21.49
    Top-90 2.71 241.53 7.13
    Top-100 1.47 229.58 5.86
    Avg 10.12 260.16 13.37
    下載: 導(dǎo)出CSV

    表  3  區(qū)分度實(shí)驗(yàn)結(jié)果

    方法 p2p-Gnutella08 WiKi-Vote CA-HepTh
    DC 0.01206 0.04216 0.00557
    LTC 0.05055 0.05205 0.04454
    BC 0.71861 0.64216 0.40376
    LC 0.85129 0.80689 0.72161
    LDDC 0.60705 0.60899 0.32672
    TIM 0.98905 0.99874 0.91789
    下載: 導(dǎo)出CSV

    表  4  運(yùn)行時(shí)間 (s)

    數(shù)據(jù)集 傳播概率 DH DD 隨機(jī) SCC TIM NG CCA(2) DeC
    p2p-Gnutella08 p=0.010 0.022 0.027 0.002 0.025 0.258 0.406 0.041 0.046
    p=0.020 0.028 0.029 0.003 0.036 0.278 0.415 0.043 0.058
    p=0.030 0.038 0.034 0.005 0.038 0.320 0.423 0.045 0.063
    CA-HepTH p=0.010 0.014 0.017 0.0016 0.019 0.194 0.573 0.054 0.030
    p=0.025 0.021 0.021 0.0025 0.027 0.239 0.645 0.062 0.059
    p=0.050 0.038 0.045 0.0062 0.034 0.325 0.792 0.076 0.161
    WiKi-Vote p=0.001 0.141 0.136 0.011 0.157 0.517 1.921 0.434 0.412
    p=0.005 0.249 0.265 0.026 0.261 0.651 1.947 0.481 0.526
    p=0.010 0.793 0.776 0.480 0.772 1.153 2.434 0.975 1.001
    下載: 導(dǎo)出CSV
  • 劉陽, 季新生, 劉彩霞. 一種基于邊界節(jié)點(diǎn)識別的復(fù)雜網(wǎng)絡(luò)局部社區(qū)發(fā)現(xiàn)算法[J]. 電子與信息學(xué)報(bào), 2014, 36(12): 2809–2815. doi: 10.3724/SP.J.1146.2013.01955

    LIU Yang, JI Xinsheng, and LIU Caixia. Detecting local community structure based on the identification of boundary nodes in complex networks[J]. Journal of Electronics&Information Technology, 2014, 36(12): 2809–2815. doi: 10.3724/SP.J.1146.2013.01955
    田晶, 方華強(qiáng), 劉佳佳, 等. 運(yùn)用復(fù)雜網(wǎng)絡(luò)方法分析城市道路網(wǎng)的魯棒性[J]. 武漢大學(xué)學(xué)報(bào): 信息科學(xué)版, 2019, 44(5): 771–777. doi: 10.13203/j.whugis20150334

    TIAN Jing, FANG Huaqiang, LIU Jiajia, et al. Robustness analysis of urban street networks using complex network method[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 771–777. doi: 10.13203/j.whugis20150334
    王凱, 劉樹新, 于洪濤, 等. 基于共同鄰居有效性的復(fù)雜網(wǎng)絡(luò)鏈路預(yù)測算法[J]. 電子科技大學(xué)學(xué)報(bào), 2019, 48(3): 432–439. doi: 10.3969/j.issn.1001-0548.2019.03.020

    WANG Kai, LIU Shuxin, YU Hongtao, et al. Predicting missing links of complex network via effective common neighbors[J]. Journal of University of Electronic Science and Technology of China, 2019, 48(3): 432–439. doi: 10.3969/j.issn.1001-0548.2019.03.020
    韓忠明, 劉雯, 李夢琪, 等. 基于節(jié)點(diǎn)向量表達(dá)的復(fù)雜網(wǎng)絡(luò)社團(tuán)劃分算法[J]. 軟件學(xué)報(bào), 2019, 30(4): 1045–1061. doi: 10.13328/j.cnki.jos.005387

    HAN Zhongming, LIU Wen, LI Mengqi, et al. Community detection algorithm based on node embedding vector representation[J]. Journal of Software, 2019, 30(4): 1045–1061. doi: 10.13328/j.cnki.jos.005387
    韓忠明, 陳炎, 李夢琪, 等. 一種有效的基于三角結(jié)構(gòu)的復(fù)雜網(wǎng)絡(luò)節(jié)點(diǎn)影響力度量模型[J]. 物理學(xué)報(bào), 2016, 65(16): 168901. doi: 10.7498/aps.65.168901

    HAN Zhongming, CHEN Yan, LI Mengqi, et al. An efficient node influence metric based on triangle in complex networks[J]. Acta Physica Sinica, 2016, 65(16): 168901. doi: 10.7498/aps.65.168901
    楊青林, 王立夫, 李歡, 等. 基于相對距離的復(fù)雜網(wǎng)絡(luò)譜粗?；椒╗J]. 物理學(xué)報(bào), 2019, 68(10): 100501. doi: 10.7498/aps.68.20181848

    YANG Qinglin, WANG Lifu, LI Huan, et al. A spectral coarse graining algorithm based on relative distance[J]. Acta Physica Sinica, 2019, 68(10): 100501. doi: 10.7498/aps.68.20181848
    林冠強(qiáng), 莫天文, 葉曉君, 等. 基于TOPSIS和CRITIC法的電網(wǎng)關(guān)鍵節(jié)點(diǎn)識別[J]. 高電壓技術(shù), 2018, 44(10): 3383–3389. doi: 10.13336/j.1003-6520.hve.20180925030

    LIN Guanqiang, MO Tianwen, YE Xiaojun, et al. Critical node identification of power networks based on TOPSIS and CRITIC methods[J]. High Voltage Engineering, 2018, 44(10): 3383–3389. doi: 10.13336/j.1003-6520.hve.20180925030
    CHRISTAKIS N A and FOWLER J H. Social contagion theory: Examining dynamic social networks and human behavior[J]. Statistics in Medicine, 2013, 32(4): 556–577. doi: 10.1002/sim.5408
    CHRISTAKIS N A and FOWLER J H. Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives[M]. New York: Little, Brown and Company, 2009: 30–117.
    許小可, 胡海波, 張倫, 等. 社交網(wǎng)絡(luò)上的計(jì)算傳播學(xué)[M]. 北京: 高等教育出版社, 2015: 163–198.

    XU Xiaoke, HU Haibo, ZHANG Lun, et al. Computational Communication in Social Networks[M]. Beijing: Higher Education Press, 2015: 163–198.
    陳曉龍. 社會網(wǎng)絡(luò)影響力最大化算法及其傳播模型研究[D]. [碩士論文], 哈爾濱工程大學(xué), 2016: 20–22.

    CHEN Xiaolong. Research on influence maximization and diffusion model in social networks[D]. [Master dissertation], Harbin Engineering University, 2016: 20–22.
    王俊, 余偉, 胡亞慧, 等. 基于3-layer中心度的社交網(wǎng)絡(luò)影響力最大化算法[J]. 計(jì)算機(jī)科學(xué), 2014, 41(1): 59–63. doi: 10.3969/j.issn.1002-137X.2014.01.009

    WANG Jun, XU Wei, HU Yahui, et al. Heuristic algorithm based on 3-layer centrality for influence maximization in social networks[J]. Computer Science, 2014, 41(1): 59–63. doi: 10.3969/j.issn.1002-137X.2014.01.009
    CHEN Duanbing, Lü Linyuan, SHANG Mingsheng, et al. Identifying influential nodes in complex networks[J]. Physica A:Statistical Mechanics and its Applications, 2012, 391(4): 1777–1787. doi: 10.1016/j.physa.2011.09.017
    FREEMAN L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1): 35–41. doi: 10.2307/3033543
    LI Jianxin, LIU Chengfei, XU J X, et al. Personalized influential topic search via social network summarization[C]. The 33rd IEEE International Conference on Data Engineering, San Diego, USA, 2016: 1820–1834. doi: 10.1109/ICDE.2017.15.
    CHEN Wei, WANG Yajun, and YANG Siyu. Efficient influence maximization in social networks[C]. The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 2009: 199–208. doi: 10.1145/1557019.1557047.
    KEMPE D, KLEINBERG J, and TARDOS é. Maximizing the spread of influence through a social network[C]. The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, USA, 2003: 137–146. doi: 10.1145/956750.956769.
    曹玖新, 董丹, 徐順, 等. 一種基于k-核的社會網(wǎng)絡(luò)影響最大化算法[J]. 計(jì)算機(jī)學(xué)報(bào), 2015, 38(2): 238–248. doi: 10.3724/SP.J.1016.2015.00238

    CAO Jiuxin, DONG Dan, XU Shun, et al. A k-core based algorithm for influence maximization in social networks[J]. Chinese Journal of Computers, 2015, 38(2): 238–248. doi: 10.3724/SP.J.1016.2015.00238
    IBNOULOUAFI A and EL HAZITI M. Density centrality: Identifying influential nodes based on area density formula[J]. Chaos,Solitons&Fractals, 2018, 114: 69–80. doi: 10.1016/j.chaos.2018.06.022
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  • 收稿日期:  2019-06-17
  • 修回日期:  2020-02-02
  • 網(wǎng)絡(luò)出版日期:  2020-02-20
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