本文選題：鏈接權(quán)重 + 數(shù)據(jù)場(chǎng)��； 參考：《內(nèi)蒙古大學(xué)》2014年碩士論文

【摘要】：隨著信息技術(shù)的發(fā)展,產(chǎn)生了大規(guī)模的網(wǎng)絡(luò)數(shù)據(jù),這為進(jìn)行大規(guī)模的網(wǎng)絡(luò)分析研究提供了充足的數(shù)據(jù)。近幾年網(wǎng)絡(luò)挖掘的研究迅速崛起,并發(fā)展成為一個(gè)很熱門的研究領(lǐng)域。鏈接預(yù)測(cè)是網(wǎng)絡(luò)分析的一個(gè)重要部分,是一個(gè)具有挑戰(zhàn)性的研究方向。本文圍繞網(wǎng)絡(luò)數(shù)據(jù)挖掘領(lǐng)域,針對(duì)鏈接預(yù)測(cè)任務(wù)展開了深入的研究。在對(duì)現(xiàn)有鏈接預(yù)測(cè)算法分析的基礎(chǔ)上,重點(diǎn)研究了基于結(jié)構(gòu)相似性的鏈接預(yù)測(cè)算法。針對(duì)現(xiàn)有基于結(jié)構(gòu)相似性的鏈接預(yù)測(cè)方法忽略了網(wǎng)絡(luò)拓?fù)浔旧礞溄訌?qiáng)度的信息,帶權(quán)的拓?fù)渎窂椒椒ㄖ袡?quán)值較難確定等缺陷,提出基于鏈接重要性和數(shù)據(jù)場(chǎng)的鏈接預(yù)測(cè)算法。該方法將所有鏈接邊賦予不同的鏈接權(quán)重,同時(shí)考慮潛在鏈接節(jié)點(diǎn)間的相互影響,對(duì)部分沒(méi)有鏈接的節(jié)點(diǎn)進(jìn)行鏈接預(yù)估計(jì),最后利用數(shù)據(jù)場(chǎng)勢(shì)函數(shù)計(jì)算兩節(jié)點(diǎn)間的相似值。實(shí)驗(yàn)結(jié)果表明,該方法整體上提高了預(yù)測(cè)準(zhǔn)確性,且參數(shù)確定簡(jiǎn)單,有很高的實(shí)用價(jià)值。另外針對(duì)網(wǎng)絡(luò)通常是動(dòng)態(tài)變化,且網(wǎng)絡(luò)規(guī)模通常很大,而在應(yīng)用中,實(shí)時(shí)性要求又很高,現(xiàn)有的算法復(fù)雜度更新代價(jià)又較高,難以達(dá)到實(shí)時(shí)要求的現(xiàn)狀,提出了網(wǎng)絡(luò)的特定存儲(chǔ)方式以及增量計(jì)算方法,達(dá)到低代價(jià)更新網(wǎng)絡(luò)的目的。
[Abstract]:With the development of information technology, large-scale network data is produced, which provides sufficient data for large-scale network analysis. In recent years, the research of network mining has risen rapidly, and has become a very popular research field. Link prediction is an important part of network analysis and a challenging research direction. This paper focuses on the research of link prediction task in the field of network data mining. Based on the analysis of existing link prediction algorithms, a link prediction algorithm based on structural similarity is studied. The existing link prediction methods based on structural similarity ignore the information of the link strength of the network topology itself and the weight value of the weighted topological path method is difficult to determine. A link prediction algorithm based on link importance and data field is proposed. In this method, all link edges are given different link weights, and the interaction between potential link nodes is taken into account, and some nodes without links are pre-estimated. Finally, the similarity between two nodes is calculated by using the potential function of the data field. The experimental results show that the method improves the accuracy of prediction, and the parameter determination is simple, and has high practical value. In addition, the network is usually dynamic, and the network scale is usually very large, but in the application, the real-time requirement is very high, the cost of updating the existing algorithm complexity is high, it is difficult to meet the real-time requirements. The special storage mode and incremental computing method are proposed to update the network at low cost.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前3條

1 李玉華;肖海嶺;李棟才;李瑞軒;;基于鏈接重要性的動(dòng)態(tài)鏈接預(yù)測(cè)方法研究[J];計(jì)算機(jī)研究與發(fā)展;2011年S3期

2 東昱曉;柯慶;吳斌;;基于節(jié)點(diǎn)相似性的鏈接預(yù)測(cè)[J];計(jì)算機(jī)科學(xué);2011年07期

3 崔愛香;傅彥;尚明生;陳端兵;周濤;;復(fù)雜網(wǎng)絡(luò)局部結(jié)構(gòu)的涌現(xiàn):共同鄰居驅(qū)動(dòng)網(wǎng)絡(luò)演化[J];物理學(xué)報(bào);2011年03期

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本文編號(hào)：1887003