本文選題：異質(zhì)關(guān)系網(wǎng)絡(luò) + 時態(tài)語義路徑　； 參考：《中南大學(xué)》2014年碩士論文

【摘要】：社會關(guān)系網(wǎng)絡(luò)分析已經(jīng)成為當(dāng)今最熱門的研究領(lǐng)域之一。在社會關(guān)系網(wǎng)絡(luò)建立的過程中,由于數(shù)據(jù)來源的多樣化、數(shù)據(jù)規(guī)模的日益擴大以及事務(wù)信息的不完備、人物的個人基本信息缺失、信息模式與結(jié)構(gòu)的差異以及曾命名現(xiàn)象等因素,導(dǎo)致關(guān)系網(wǎng)絡(luò)中人物關(guān)系出現(xiàn)混亂,這將使得在社會關(guān)系網(wǎng)絡(luò)中人物的唯一性難以識別�；谄胀P(guān)系網(wǎng)絡(luò)結(jié)構(gòu)參數(shù)計算的傳統(tǒng)社會關(guān)系網(wǎng)絡(luò)分析方法難以解決上述問題。本文在異質(zhì)關(guān)系網(wǎng)絡(luò)以及語義網(wǎng)絡(luò)基礎(chǔ)之上,提出了一種基于異質(zhì)關(guān)系網(wǎng)絡(luò)的時態(tài)語義路徑相似度計算的人物唯一性度量方法以及基于結(jié)構(gòu)誤差計算的相同人物合并校驗策略。 本文調(diào)研了傳統(tǒng)社會關(guān)系網(wǎng)絡(luò)分析中常用的普通關(guān)系網(wǎng)絡(luò)模型以及異質(zhì)關(guān)系網(wǎng)絡(luò)模型,分析了普通關(guān)系網(wǎng)絡(luò)在大規(guī)模社會關(guān)系網(wǎng)絡(luò)分析中的局限性,提出了基于異質(zhì)關(guān)系網(wǎng)絡(luò)的時態(tài)語義路徑相似度的人物唯一性度量方法。該方法結(jié)合語義網(wǎng)絡(luò)的特征提出了時態(tài)語義網(wǎng)絡(luò)的概念,利用人物節(jié)點對之間的時態(tài)語義路徑相似度計算以及基于相似度閾值的唯一性人物過濾策略來實現(xiàn)人物唯一性識別,通過包含了多樣性的關(guān)系語義信息的相似度計算實現(xiàn)了人物節(jié)點相似性及唯一性的度量,實驗結(jié)果表明該方法能夠在大規(guī)模社會關(guān)系網(wǎng)絡(luò)中準(zhǔn)確度量人物節(jié)點的唯一性,驗證了方法的有效性。 針對關(guān)系網(wǎng)絡(luò)中具有高相似度的人物節(jié)點,本文提出了關(guān)系網(wǎng)絡(luò)結(jié)構(gòu)誤差計算方法,該方法根據(jù)合并前后人物節(jié)點的度的變化計算節(jié)點對的結(jié)構(gòu)誤差,根據(jù)結(jié)構(gòu)誤差的取值判斷人物節(jié)點對在關(guān)系結(jié)構(gòu)上是否完全相同。通過該方法將具有完全相同的關(guān)系結(jié)構(gòu)的人物節(jié)點篩選出來。然后,用相同人物節(jié)點合并策略對其進(jìn)行合并,從而實現(xiàn)了關(guān)系網(wǎng)絡(luò)中人物校正的目的。本文通過對一個含有多種學(xué)術(shù)活動信息的學(xué)術(shù)關(guān)系網(wǎng)絡(luò)數(shù)據(jù)進(jìn)行實驗分析,驗證了該方法在關(guān)系網(wǎng)絡(luò)中相同人物校正中的有效性。
[Abstract]:Social network analysis has become one of the most popular research fields. In the process of establishing social relations network, due to the diversification of data sources, the increasing expansion of data scale and incomplete transaction information, the basic personal information of people is missing. The difference of information pattern and structure, as well as the phenomenon of once naming, lead to the confusion of the relationship between people in the relationship network, which will make it difficult to identify the uniqueness of the person in the network of social relations. It is difficult to solve the above problems by traditional social network analysis method based on the calculation of common relational network structure parameters. On the basis of heterogeneous relation network and semantic network, this paper presents a new method to measure the similarity of temporal semantic path based on heterogeneous relation network and a new method of merging and checking the same person based on the calculation of structural error. This paper investigates the common relationship network model and heterogeneous relationship network model commonly used in the traditional social relationship network analysis, and analyzes the limitations of the common relationship network in the large-scale social network analysis. In this paper, we propose a method to measure the similarity of temporal semantic paths based on heterogeneous relational networks. Based on the features of semantic network, the concept of temporal semantic network is put forward in this method. The similarity calculation of temporal semantic path between human nodes and the unique character filtering strategy based on similarity threshold are used to realize the identity recognition. The similarity and uniqueness of human nodes are measured by the similarity calculation of relational semantic information including diversity. The experimental results show that the proposed method can measure the uniqueness of human nodes accurately in large-scale social networks. The validity of the method is verified. In this paper, a method of calculating the error of relational network structure is proposed for the person nodes with high similarity in the relational network. This method calculates the structural errors of the nodes according to the change of the degree of the nodes before and after the merging. According to the value of the structure error, it is determined whether the human nodal pair is exactly the same in the relation structure. By this method, the character nodes with exactly the same relationship structure are filtered out. Then, the same person node merging strategy is used to achieve the goal of character correction in the relational network. In this paper, the validity of this method in the correction of the same person in the relational network is verified by the experimental analysis of an academic relationship network data containing a variety of academic activity information.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09;TP391.1

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本文編號：2061455