本文選題：復(fù)雜網(wǎng)絡(luò)　切入點(diǎn)：鏈路預(yù)測　出處：《南京理工大學(xué)》2017年碩士論文

【摘要】：鏈路預(yù)測(Link Prediction)問題是數(shù)據(jù)挖掘領(lǐng)域的研究方向之一,因其有重要的理論研究意義和廣泛的應(yīng)用價(jià)值而受到各個(gè)領(lǐng)域的關(guān)注。鏈路預(yù)測指如何根據(jù)已知網(wǎng)絡(luò)的節(jié)點(diǎn)屬性和網(wǎng)絡(luò)拓?fù)涞刃畔?預(yù)測網(wǎng)絡(luò)節(jié)點(diǎn)對之間缺失的連邊和未來可能產(chǎn)生的連邊。近年來,隨著復(fù)雜網(wǎng)絡(luò)理論體系的不斷發(fā)展,基于網(wǎng)絡(luò)結(jié)構(gòu)的鏈路預(yù)測算法漸漸成為研究焦點(diǎn)。本文的主要內(nèi)容如下:首先,介紹了復(fù)雜網(wǎng)絡(luò)的基礎(chǔ)知識,包括簡單網(wǎng)絡(luò)、加權(quán)網(wǎng)絡(luò)和多層網(wǎng)絡(luò)的統(tǒng)計(jì)特征和演化模型;其次,介紹了鏈路預(yù)測問題的研究背景、研究現(xiàn)狀以及現(xiàn)有的典型鏈路預(yù)測算法的核心思想;然后,介紹了信息熵的基本概念和性質(zhì),推導(dǎo)出復(fù)雜網(wǎng)絡(luò)路徑熵的表達(dá)式,并將路徑熵用于節(jié)點(diǎn)相似性的度量,提出了一種基于網(wǎng)絡(luò)局部拓?fù)浣Y(jié)構(gòu)的路徑熵(Path Entropy,PE)相似性指標(biāo)。實(shí)驗(yàn)結(jié)果表明,該指標(biāo)比經(jīng)典的準(zhǔn)局部和局部指標(biāo)有更好的預(yù)測性能;接著,將路徑熵和路徑的權(quán)重結(jié)合,提出了一種適用于加權(quán)網(wǎng)絡(luò)的鏈路預(yù)測指標(biāo),即加權(quán)路徑熵指標(biāo)(Weighted Path Entropy index,WPE)。實(shí)驗(yàn)結(jié)果表明,該指標(biāo)比現(xiàn)有的加權(quán)指標(biāo)有更好的預(yù)測性能。進(jìn)一步考慮到現(xiàn)實(shí)復(fù)雜網(wǎng)絡(luò)中連邊類型的異質(zhì)性和網(wǎng)絡(luò)結(jié)構(gòu)的層次性,結(jié)合路徑熵的概念,提出了一種適用于多層復(fù)雜網(wǎng)絡(luò)的鏈路預(yù)測方案;最后,針對大規(guī)模復(fù)雜網(wǎng)絡(luò)鏈路預(yù)測的難點(diǎn),提出了基于共同鄰居下界的并行計(jì)算預(yù)測模型,著重分析和預(yù)測網(wǎng)絡(luò)"熱點(diǎn)區(qū)域"中的連邊。同時(shí),提出了自預(yù)測性指標(biāo)以度量鏈路預(yù)測算法的性能和評估網(wǎng)絡(luò)的可預(yù)測性。
[Abstract]:Link prediction (Link prediction) is one of the research directions in the field of data mining. It has been paid attention to by many fields because of its important theoretical research significance and wide application value.Link prediction refers to how to predict the missing links between network nodes and the possible future connected edges based on the known network node properties and network topology information.In recent years, with the development of complex network theory system, link prediction algorithm based on network structure has gradually become the focus of research.The main contents of this paper are as follows: firstly, the basic knowledge of complex network is introduced, including the statistical characteristics and evolution model of simple network, weighted network and multilayer network, secondly, the research background of link prediction problem is introduced.Then, the basic concepts and properties of information entropy are introduced, the expression of path entropy of complex network is derived, and the path entropy is used to measure the similarity of nodes.A path entropy path EntropyPe similarity index based on local network topology is proposed.The experimental results show that this index has better prediction performance than the classical quasi-local and local indexes, and then a link prediction index suitable for weighted network is proposed by combining path entropy with path weight.That is weighted Path Entropy index.The experimental results show that this index has better prediction performance than the existing weighted index.Considering the heterogeneity of the connected edge type and the hierarchy of the network structure in the real complex network, a link prediction scheme suitable for the multilayer complex network is proposed by combining the concept of path entropy.Aiming at the difficulties of link prediction in large-scale and complex networks, a parallel computing prediction model based on common neighbor lower bound is proposed, with emphasis on the analysis and prediction of the connected edges in the "hot spot region" of the network.At the same time, a self-predictive index is proposed to measure the performance of the link prediction algorithm and to evaluate the predictability of the network.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 許忠奇;濮存來;Rajput Ramiz Sharafat;李倫波;楊健;;Entropy-based link prediction in weighted networks[J];Chinese Physics B;2017年01期

2 傅穎斌;陳羽中;;基于鏈路預(yù)測的微博用戶關(guān)系分析[J];計(jì)算機(jī)科學(xué);2014年02期

3 白萌;胡柯;唐翌;;Link prediction based on a semi-local similarity index[J];Chinese Physics B;2011年12期

4 呂琳媛;;復(fù)雜網(wǎng)絡(luò)鏈路預(yù)測[J];電子科技大學(xué)學(xué)報(bào);2010年05期

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本文編號：1698909