本文關(guān)鍵詞： 社會(huì)網(wǎng)絡(luò) 鏈路預(yù)測(cè) 加權(quán)網(wǎng)絡(luò) 時(shí)間感知　出處：《中南大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：鏈路預(yù)測(cè)是社會(huì)網(wǎng)絡(luò)研究中的一個(gè)重要分支,其主要任務(wù)是通過(guò)當(dāng)前網(wǎng)絡(luò)中存在的鏈路對(duì)未來(lái)有可能出現(xiàn)的鏈路進(jìn)行預(yù)測(cè)。近年來(lái),社會(huì)網(wǎng)絡(luò)中的鏈路預(yù)測(cè)問(wèn)題吸引了越來(lái)越多的研究人員關(guān)注,它在現(xiàn)代科學(xué)中不僅具有深刻的理論意義,而且具有極大的應(yīng)用價(jià)值。 由于現(xiàn)有的鏈路預(yù)測(cè)算法主要應(yīng)用于簡(jiǎn)單的靜態(tài)無(wú)權(quán)網(wǎng)絡(luò)中,沒(méi)有考慮權(quán)重以及時(shí)間序列對(duì)于鏈路預(yù)測(cè)的影響,所以不能很好地適應(yīng)復(fù)雜網(wǎng)絡(luò)的情形。為了解決這一問(wèn)題,本文針對(duì)含權(quán)網(wǎng)絡(luò),提出了節(jié)點(diǎn)權(quán)重和鏈路權(quán)重的概念,在擴(kuò)展和結(jié)合已有鏈路預(yù)測(cè)算法的基礎(chǔ)上提出了新的鏈路預(yù)測(cè)算法。此外,針對(duì)時(shí)間感知網(wǎng)絡(luò),提出了時(shí)間因子的概念,用來(lái)量化時(shí)間序列中的時(shí)間因素以幫助計(jì)算節(jié)點(diǎn)間的相似度；最后,綜合權(quán)重思想和時(shí)間因子的概念提出了基于時(shí)間感知的加權(quán)網(wǎng)絡(luò)鏈路預(yù)測(cè)算法。 本文針對(duì)加權(quán)網(wǎng)絡(luò)和時(shí)間感知網(wǎng)絡(luò),分別選取了若干個(gè)真實(shí)數(shù)據(jù)集對(duì)提出的鏈路預(yù)測(cè)算法進(jìn)行了分析比較。實(shí)驗(yàn)結(jié)果表明,本文所提出的加權(quán)鏈路預(yù)測(cè)算法相比無(wú)權(quán)鏈路預(yù)測(cè)算法有更好的精度,基于時(shí)間感知的鏈路預(yù)測(cè)算法比不含時(shí)間因子的鏈路預(yù)測(cè)算法效果更明顯,結(jié)合了二者優(yōu)點(diǎn)的加權(quán)時(shí)間感知鏈路預(yù)測(cè)算法也獲得了較高的預(yù)測(cè)精度。圖21幅,表10個(gè),參考文獻(xiàn)65篇。
[Abstract]:Link prediction is an important branch of social network research. Its main task is to predict the possible future links through the existing links in the current network. The problem of link prediction in social networks has attracted more and more researchers' attention. It not only has profound theoretical significance in modern science, but also has great application value. Because the existing link prediction algorithms are mainly used in simple static unweighted networks and do not consider the influence of weight and time series on link prediction, they can not adapt well to the situation of complex networks. In this paper, the concepts of node weight and link weight are proposed for weighted networks, and a new link prediction algorithm is proposed on the basis of extending and combining existing link prediction algorithms. The concept of time factor is proposed to quantify the time factor in time series to help calculate the similarity between nodes. Finally, a weighted network link prediction algorithm based on time perception is proposed based on the concept of weight and time factor. In this paper, several real data sets are selected to analyze and compare the proposed link prediction algorithms for weighted networks and time-aware networks. The experimental results show that, The weighted link prediction algorithm proposed in this paper has better precision than the unweighted link prediction algorithm. The link prediction algorithm based on time perception is more effective than the link prediction algorithm without time factor. The weighted time perceptual link prediction algorithm which combines the advantages of the two methods also has a high prediction accuracy. Fig. 21, table 10, references 65.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.09

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本文編號(hào)：1511791