本文關鍵詞： 供應鏈網(wǎng)絡 合作演化 鏈路預測 網(wǎng)絡結構 能源供應鏈 相似性指標 精確度 耦合　出處：《智能系統(tǒng)學報》2017年02期 　論文類型：期刊論文

【摘要】：應用供應鏈網(wǎng)絡結構或節(jié)點的屬性信息預測未產生鏈接的節(jié)點企業(yè)合作的可能性是鏈路預測應用供應鏈網(wǎng)絡合作演化分析的關鍵,利用鏈路預測的理論框架和評價方法,借助5種相似性指標對能源供應鏈網(wǎng)絡合作連邊演化進行預測。研究結果表明:當使用供應鏈網(wǎng)絡結構屬性作為單一相似性指標時,RWR指標的預測效果最好;耦合指標預測精確度要比單獨考慮單一指標時有顯著提高;RWR指標和Katz指標耦合效果要優(yōu)于和CN指標、PA指標、LP指標耦合效果,且RWR指標在耦合算法中起到主導性作用;與直接建立網(wǎng)絡演化模型相比,鏈路預測在分析供應鏈網(wǎng)絡合作演化機制上更為有效。
[Abstract]:The application of the network structure of supply chain or the attribute information of nodes to predict the possibility of the cooperation of node enterprises without producing links is the key to the analysis of the evolution of cooperation in the application of supply chain network in link prediction. The theoretical framework and evaluation method of link prediction are used. With the help of five similarity indicators, the evolution of energy supply chain network cooperation continuity is predicted. The results show that: when the supply chain network structure attribute is used as a single similarity index. The prediction effect of RWR index is the best. The prediction accuracy of coupling index is significantly higher than that of single index. The coupling effect of RWR index and Katz index is better than that of CN index, PA index and LP index, and RWR index plays a leading role in the coupling algorithm. Compared with direct network evolution model, link prediction is more effective in analyzing the evolution mechanism of supply chain network cooperation.
【作者單位】： 桂林電子科技大學商學院;
【基金】：國家自然科學基金項目(71662007) 廣西哲學社會科學研究課題(15BJY016) 桂林電子科技大學研究生教育創(chuàng)新計劃項目(2016YJCX61)
【分類號】：F274;F426.2;TP301.6
【正文快照】： ZHANG Xuelong,WANG Junjin(School of Business,Guilin University of Electronic Technology,Guilin 541004,China)預測是所有的科學學科所不能回避的問題。鏈路預測是數(shù)據(jù)挖掘的研究方向之一,尤其在計算機領域早有較深入的研究,其研究思路主要是基于馬爾可夫鏈和機器學習[1，

本文編號：1485095