本文選題：鏈接預(yù)測　+ hinge函數(shù)　引自：《小型微型計算機系統(tǒng)》2017年07期

【摘要】：快速擴展的互聯(lián)網(wǎng)形成了具有高維、稀疏和冗余特性的復(fù)雜網(wǎng)絡(luò).因此需要有效的技術(shù)從這些復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)中提取出最為重要的信息進行鏈接預(yù)測,以便為用戶服務(wù).本文提出一種基于AUC(Area under Curve)優(yōu)化的鏈接預(yù)測算法.在該算法中,將AUC作為優(yōu)化的目標函數(shù),將鏈接預(yù)測問題轉(zhuǎn)化為二分分類問題.將頂點之間是否存在鏈接作為它所在的類的標號.通過優(yōu)化AUC來進行二分分類,使用鉸鏈函數(shù)按隨機次梯度下降算法迭代更新權(quán)重矩陣.最后在一些來自不同領(lǐng)域的真實網(wǎng)絡(luò)上對本算法進行了測試.實驗結(jié)果表明,本算法與其他算法的結(jié)果相比可以實現(xiàn)更高質(zhì)量的預(yù)測.
[Abstract]:The rapid expansion of the Internet has formed a complex network with high dimensional, sparse and redundant characteristics.Therefore, effective technology is needed to extract the most important information from these complex network data for link prediction in order to serve users.This paper presents a link prediction algorithm based on AUC(Area under Curve optimization.In this algorithm, AUC is taken as the optimization objective function, and the link prediction problem is transformed into a binary class problem.Label a link between vertices as the class in which it resides.By optimizing AUC to carry out binary classification, the hinge function is used to iteratively update the weight matrix according to the stochastic subgradient descent algorithm.Finally, the algorithm is tested on some real networks from different fields.Experimental results show that this algorithm can achieve higher quality prediction than other algorithms.
【作者單位】： 南京航空航天大學計算機科學與技術(shù)學院;揚州大學信息工程學院;
【基金】：國家自然科學基金項目(61379066,61070047,61379064,61472344,61402395)資助 江蘇省自然科學基金項目(BK20130452,BK2012672,BK2012128,BK20140492)資助
【分類號】：O157.5
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本文編號：1730826