本文選題：復(fù)雜網(wǎng)絡(luò)　切入點(diǎn)：網(wǎng)絡(luò)科學(xué)　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：鏈路預(yù)測(cè)問(wèn)題是數(shù)據(jù)挖掘和知識(shí)發(fā)現(xiàn)領(lǐng)域的一個(gè)開(kāi)放性問(wèn)題,吸引了來(lái)自不同研究團(tuán)體的研究人員的關(guān)注。鏈路預(yù)測(cè)的研究對(duì)于理解現(xiàn)實(shí)世界的網(wǎng)絡(luò)類系統(tǒng)的組成和演化具有深遠(yuǎn)的科學(xué)意義。另一方面,優(yōu)秀的鏈路預(yù)測(cè)算法在不同的領(lǐng)域具有廣泛的應(yīng)用,例如生物網(wǎng)絡(luò)中鑒定可能的蛋白質(zhì)-蛋白質(zhì)相互作用、在線社交網(wǎng)絡(luò)中為用戶推薦潛在的好友、在電子商務(wù)系統(tǒng)中提供個(gè)性化的推薦服務(wù)。本文中,我們從不同類型的網(wǎng)絡(luò)深入細(xì)致地研究了鏈路預(yù)測(cè)問(wèn)題。具體地,本文的主要內(nèi)容和成果如下:1.我們從信息論的角度重新審視了網(wǎng)絡(luò)結(jié)構(gòu)在預(yù)測(cè)缺失鏈接中的作用,并提出了一個(gè)基于信息論的鏈路預(yù)測(cè)模型來(lái)同時(shí)利用多種結(jié)構(gòu)特征。根據(jù)提出的模型,我們利用一種刻畫(huà)節(jié)點(diǎn)的局部結(jié)構(gòu),即鄰居集合,提出了一個(gè)叫做鄰居集合信息(NSI)的預(yù)測(cè)指標(biāo)。根據(jù)我們的實(shí)驗(yàn)結(jié)果,和其它的相似性指標(biāo)相比,NSI指標(biāo)在十二個(gè)真實(shí)網(wǎng)絡(luò)中表現(xiàn)良好。以NSI指標(biāo)為例,我們還給出了關(guān)于信息論模型的深入討論。2.我們根據(jù)局部網(wǎng)絡(luò)結(jié)構(gòu)帶來(lái)的互信息提出了一個(gè)適用于加權(quán)網(wǎng)絡(luò)的加權(quán)互信息模型,它同時(shí)充分利用了結(jié)構(gòu)和權(quán)重信息。我們?cè)谒膫�(gè)真實(shí)網(wǎng)絡(luò)中進(jìn)行了實(shí)證實(shí)驗(yàn),結(jié)果表明相較于傳統(tǒng)的無(wú)權(quán)指標(biāo)和典型的加權(quán)指標(biāo),提出的模型能夠提供更準(zhǔn)確的預(yù)測(cè)。進(jìn)一步地,我們從另外一個(gè)角度揭示了弱鏈接在鏈路預(yù)測(cè)中的影響。3.我們根據(jù)一種網(wǎng)絡(luò)的局部結(jié)構(gòu),即節(jié)點(diǎn)的鄰居集合,設(shè)計(jì)了一種權(quán)重預(yù)測(cè)方法,并在兩種情況下評(píng)估了該方法的預(yù)測(cè)效果。在第一種情況下,一些連邊連同它們的權(quán)重同時(shí)缺失;而在第二種情況下,所有的連邊都存在只有部分連邊的權(quán)重缺失。在六個(gè)真實(shí)網(wǎng)絡(luò)的實(shí)證實(shí)驗(yàn)表明我們的方法在這兩種情況下均能夠給出準(zhǔn)確的連邊權(quán)重預(yù)測(cè)。
[Abstract]:Link prediction is an open problem in the field of data mining and knowledge discovery. It has attracted the attention of researchers from different research groups. The study of link prediction is of profound scientific significance in understanding the composition and evolution of network systems in the real world. On the other hand, Excellent link prediction algorithms are widely used in a variety of fields, such as identifying potential protein-protein interactions in biological networks, recommending potential friends to users in online social networks, In this paper, we study the problem of link prediction from different types of networks. The main contents and results of this paper are as follows: 1.We re-examine the role of network structure in predicting missing links from the perspective of information theory. A link prediction model based on information theory is proposed to simultaneously utilize various structural features. According to the proposed model, we use a local structure of nodes, that is, neighbor set, to describe the local structure of nodes. A prediction index called neighbor set Information (NSI) is proposed. According to our experimental results, compared with other similarity indexes, the NSI index performs well in twelve real networks. Take the NSI index as an example, We also give an in-depth discussion on the information theory model. 2. We propose a weighted mutual information model for weighted networks according to the mutual information brought by the local network structure. It makes full use of both structure and weight information. We have carried out empirical experiments in four real networks, and the results show that compared with the traditional unauthorized index and the typical weighted index, The proposed model can provide more accurate prediction. Further, we reveal the influence of weak link in link prediction from another angle. 3. According to the local structure of a network, that is, the neighbor set of nodes, A weight prediction method is designed and evaluated in two cases. In the first case, some connected edges are missing with their weights at the same time; in the second case, All the connected edges are only partially connected, and the experimental results of six real networks show that our method can predict the weight of connected edges accurately in both cases.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O157.5
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本文編號(hào)：1592776