本文選題：異構(gòu)網(wǎng)絡(luò) + 預(yù)測(cè)��； 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的迅速發(fā)展,社交網(wǎng)絡(luò)正在快速融入大眾的日常生活。人物個(gè)體間的關(guān)系是社交網(wǎng)絡(luò)平臺(tái)賴以生存和發(fā)展的重要組成,社交網(wǎng)絡(luò)中包含的豐富信息為輿情監(jiān)測(cè)、信息傳播研究、廣告投放等提供強(qiáng)有力的支持。但是,快速發(fā)展的網(wǎng)絡(luò)技術(shù)在帶來海量數(shù)據(jù)時(shí)也帶來虛假信息、信息缺失等噪音問題,如何在可觀察數(shù)據(jù)中預(yù)測(cè)與還原缺失的信息以及對(duì)隱含信息的挖掘成為當(dāng)前一個(gè)重要課題。社交網(wǎng)絡(luò)的信息挖掘是社會(huì)網(wǎng)絡(luò)分析的重要組成部分,也是當(dāng)前的研究熱點(diǎn)�，F(xiàn)實(shí)生活中人們通過不同類型的關(guān)系相互聯(lián)系并構(gòu)成社交網(wǎng)絡(luò),該網(wǎng)絡(luò)包含相同類型的節(jié)點(diǎn)且節(jié)點(diǎn)間關(guān)系類型多樣甚至?xí)嬖诙喾N關(guān)系,此種類型的網(wǎng)絡(luò)屬于異構(gòu)網(wǎng)絡(luò)。然而當(dāng)前社會(huì)網(wǎng)絡(luò)分析主要圍繞同構(gòu)網(wǎng)絡(luò)方面來研究,但對(duì)異構(gòu)網(wǎng)絡(luò)進(jìn)行深入分析易于發(fā)現(xiàn)更加精準(zhǔn)的隱含知識(shí)。因此,本文著眼于由人物個(gè)體以及人物間多關(guān)系所構(gòu)成的異構(gòu)社交網(wǎng)絡(luò),將關(guān)系大致分為親屬關(guān)系和社會(huì)關(guān)系兩類,針對(duì)這兩類關(guān)系的自身特點(diǎn),采用不同的策略對(duì)該異構(gòu)網(wǎng)絡(luò)中的關(guān)系進(jìn)行挖掘和預(yù)測(cè)。總體來說,本文的主要研究?jī)?nèi)容包含下述三個(gè)方面:1、針對(duì)互聯(lián)網(wǎng)大數(shù)據(jù)體量龐大,信息噪音嚴(yán)重的現(xiàn)狀,利用爬蟲程序采集與處理了大量的百度百科名人基本信息及其相關(guān)人物關(guān)系。為了更加高效存儲(chǔ)與利用這些數(shù)據(jù),采用了可以充分反映人物之間關(guān)系語義和聯(lián)系的圖數(shù)據(jù)庫(kù)Neo4j。2、分析了當(dāng)前親屬關(guān)系推理研究大多數(shù)圍繞專家系統(tǒng)或漢語言文學(xué)方面的現(xiàn)狀,無法滿足大數(shù)據(jù)時(shí)代下使用大數(shù)據(jù)量時(shí)的應(yīng)用需求。本文根據(jù)常識(shí)和社會(huì)學(xué)知識(shí),定義了常用的親屬關(guān)系及其表示方法,同時(shí)借鑒一階謂詞邏輯形式,制定了親屬關(guān)系推理規(guī)則。由于謂詞邏輯形式的推理規(guī)則無法直接用于圖數(shù)據(jù)庫(kù)上實(shí)現(xiàn)推理,因此將謂詞邏輯規(guī)則轉(zhuǎn)換為圖數(shù)據(jù)庫(kù)操作語言,極大方便了親屬關(guān)系的推理與補(bǔ)全。同時(shí)采用了三種親屬關(guān)系推理方式,以滿足親屬關(guān)系在不同應(yīng)用場(chǎng)景中的需求。3、在社會(huì)關(guān)系預(yù)測(cè)方面,首先對(duì)研究問題進(jìn)行描述和定義,明確了研究?jī)?nèi)容;其次,分析了本文研究?jī)?nèi)容與當(dāng)前異構(gòu)網(wǎng)絡(luò)關(guān)系鏈路預(yù)測(cè)的不同,提出了無需預(yù)先設(shè)定路徑模式的情況下可自動(dòng)發(fā)現(xiàn)關(guān)系路徑,并從多角度衡量關(guān)系路徑重要程度后獲得最大可達(dá)路徑的方法;再次,基于此方法建立了一種異構(gòu)網(wǎng)絡(luò)社會(huì)關(guān)系預(yù)測(cè)算法,實(shí)現(xiàn)了人物間多類型社會(huì)關(guān)系的預(yù)測(cè);最后分別運(yùn)用本文算法與相關(guān)傳統(tǒng)算法進(jìn)行關(guān)系預(yù)測(cè)實(shí)驗(yàn),經(jīng)過實(shí)驗(yàn)結(jié)果的對(duì)比和分析后進(jìn)一步證實(shí)了本文算法的有效性與準(zhǔn)確性。
[Abstract]:With the rapid development of Internet technology, social networks are rapidly integrating into the daily life of the public. The relationship between individuals is an important component of the social network platform for survival and development. The rich information contained in the social network provides strong support for public opinion monitoring, information dissemination research, advertising and so on. However, the rapid development of network technology in bringing mass data also brings false information, information loss and other noise problems, How to predict and restore missing information in observable data and how to mine hidden information has become an important issue. Social network information mining is an important part of social network analysis, and it is also a hot research topic. In real life, people connect with each other through different types of relationships and form a social network. The network contains the same type of nodes and there may even exist a variety of relationships between nodes. This type of network belongs to heterogeneous networks. However, the current social network analysis mainly focuses on isomorphic networks, but it is easy to find more accurate implicit knowledge by in-depth analysis of heterogeneous networks. Therefore, this paper focuses on the heterogeneous social network composed of personas and their multi-relationships, and divides the relationships into two types: kinship and social relations, aiming at the characteristics of these two kinds of relationships. Different strategies are adopted to mine and predict the relationships in the heterogeneous network. In general, the main research contents of this paper include the following three aspects: 1. Aiming at the current situation of big data's huge volume and serious information noise on the Internet, Using crawler program to collect and deal with a large number of Baidu encyclopedia celebrity basic information and related relationships. In order to store and utilize these data more efficiently, a graph database, Neo4j.2, which can fully reflect the relationship semantics and relations between people, is adopted. The current research on kinship reasoning is mostly focused on expert system or the present situation of Chinese language and literature. Can not meet big data era under the use of large amounts of data application requirements. Based on common sense and sociological knowledge, this paper defines the commonly used kinship relations and their representation methods, and formulates the inference rules of kinship relations with reference to the first-order predicate logic form. Because the inference rules in the form of predicate logic can not be directly used to realize reasoning on graph database, it is convenient to infer and complement the kinship relationship by converting predicate logic rules into the operation language of graph database. At the same time, three kinds of kinship reasoning methods are adopted to meet the needs of kinship in different application scenarios. In the aspect of social relationship prediction, the research problems are first described and defined, and the research content is clarified. This paper analyzes the difference between the research content and the current relationship link prediction in heterogeneous networks, and proposes that the relationship path can be automatically discovered without pre-setting the path mode. The method of maximum reachable path is obtained by measuring the importance of relational path from many angles. Thirdly, a prediction algorithm of heterogeneous network social relations is established based on this method, and the prediction of multi-type social relations among people is realized. Finally, the relationship prediction experiments are carried out by using the proposed algorithm and the traditional algorithms, and the validity and accuracy of the proposed algorithm are further verified by the comparison and analysis of the experimental results.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.09;TP311.13

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