《計算機應用研究》|Application Research of Computers

一種有效的動態網絡節點影響力模型

Effective model for measuring node influence on temporal network

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作者 韓忠明,毛銳,鄭晨燁,趙振東,段大高
機構 北京工商大學 a.計算機與信息工程學院 計算機系;b.食品安全大數據技術北京市重點實驗室,北京 100048
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文章編號 1001-3695(2019)07-009-1960-05
DOI 10.19734/j.issn.1001-3695.2017.11.0847
摘要 網絡節點影響力度量對社會網絡研究具有重要的價值。靜態網絡的影響力度量是目前研究的主要問題。實質上,社會網絡屬于動態網絡。靜態網絡節點影響力度量模型雖然可以對動態網絡不同時間點上的快照進行度量,但這種機制很難刻畫動態網絡節點影響力的變化過程。將動態網絡建模為不同時間點網絡的疊加快照,然后構建了動態網絡邊權重衰減和節點影響力衰減機制,基于該機制提出了動態網絡節點影響力模型。該模型可應用于加權或無權動態網絡節點影響力度量。為了客觀地衡量所提模型的性能,在一個模擬網絡和三個真實網絡上進行了不同實驗。實驗結果表明所提模型不僅可以較好地刻畫動態網絡節點影響力的變化過程,還可以準確度量動態網絡節點影響力。
關鍵詞 動態網絡; 節點影響力; 權重衰減
基金項目 國家自然科學基金資助項目(61170112)
北京市自然科學基金資助項目(4172016)
北京市科技計劃課題(Z161100001616004)
北京市教委科研計劃面上項目(KM201710011006)
本文URL http://www.oirznw.live/article/01-2019-07-009.html
英文標題 Effective model for measuring node influence on temporal network
作者英文名 Han Zhongming, Mao Rui, Zheng Chenye, Zhao Zhendong, Duan Dagao
機構英文名 a.Dept. of Computer,School of Computer Science & Information Engineering,b.Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing Technology & Business University,Beijing 100048,China
英文摘要 Measuring node influence on network is an important question of online social network analysis. Currently, the main related research focus on influence of static networks. Essentially, social networks are dynamic networks. Although models for measuring node influence on static networks can be used to measure node influence in different snapshots of a temporal network, it is difficult to describe the dynamic process of node influence. This paper viewed a dynamic network as a series of snapshots at different time points. It constructed the mechanisms of edge weight attenuation and node influence attenuation. Based on the attenuation mechanism, this paper proposed a model for measuring node influence on dynamic networks, which could be applied to weighted or unweighted dynamic networks. In order to evaluate the performance of proposed model, it conducted comprehensive experiments on a simulated network and three real networks. The experimental results show that proposed model can not only describe the influence dynamic process for different nodes, but also effectively measure the node influence in dynamic networks.
英文關鍵詞 dynamic network; node influence; weight attenuation
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收稿日期 2017/11/15
修回日期 2018/3/5
頁碼 1960-1964
中圖分類號 TP393
文獻標志碼 A
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