本文選題：異構網絡 + 預測��； 參考：《太原理工大學》2017年碩士論文

【摘要】：隨著互聯(lián)網技術的迅速發(fā)展,社交網絡正在快速融入大眾的日常生活。人物個體間的關系是社交網絡平臺賴以生存和發(fā)展的重要組成,社交網絡中包含的豐富信息為輿情監(jiān)測、信息傳播研究、廣告投放等提供強有力的支持。但是,快速發(fā)展的網絡技術在帶來海量數(shù)據時也帶來虛假信息、信息缺失等噪音問題,如何在可觀察數(shù)據中預測與還原缺失的信息以及對隱含信息的挖掘成為當前一個重要課題。社交網絡的信息挖掘是社會網絡分析的重要組成部分,也是當前的研究熱點。現(xiàn)實生活中人們通過不同類型的關系相互聯(lián)系并構成社交網絡,該網絡包含相同類型的節(jié)點且節(jié)點間關系類型多樣甚至會存在多種關系,此種類型的網絡屬于異構網絡。然而當前社會網絡分析主要圍繞同構網絡方面來研究,但對異構網絡進行深入分析易于發(fā)現(xiàn)更加精準的隱含知識。因此,本文著眼于由人物個體以及人物間多關系所構成的異構社交網絡,將關系大致分為親屬關系和社會關系兩類,針對這兩類關系的自身特點,采用不同的策略對該異構網絡中的關系進行挖掘和預測�？傮w來說,本文的主要研究內容包含下述三個方面:1、針對互聯(lián)網大數(shù)據體量龐大,信息噪音嚴重的現(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人物關系。為了更加高效存儲與利用這些數(shù)據,采用了可以充分反映人物之間關系語義和聯(lián)系的圖數(shù)據庫Neo4j。2、分析了當前親屬關系推理研究大多數(shù)圍繞專家系統(tǒng)或漢語言文學方面的現(xiàn)狀,無法滿足大數(shù)據時代下使用大數(shù)據量時的應用需求。本文根據常識和社會學知識,定義了常用的親屬關系及其表示方法,同時借鑒一階謂詞邏輯形式,制定了親屬關系推理規(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)據庫上實現(xiàn)推理,因此將謂詞邏輯規(guī)則轉換為圖數(shù)據庫操作語言,極大方便了親屬關系的推理與補全。同時采用了三種親屬關系推理方式,以滿足親屬關系在不同應用場景中的需求。3、在社會關系預測方面,首先對研究問題進行描述和定義,明確了研究內容;其次,分析了本文研究內容與當前異構網絡關系鏈路預測的不同,提出了無需預先設定路徑模式的情況下可自動發(fā)現(xiàn)關系路徑,并從多角度衡量關系路徑重要程度后獲得最大可達路徑的方法;再次,基于此方法建立了一種異構網絡社會關系預測算法,實現(xiàn)了人物間多類型社會關系的預測;最后分別運用本文算法與相關傳統(tǒng)算法進行關系預測實驗,經過實驗結果的對比和分析后進一步證實了本文算法的有效性與準確性。
[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.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.09;TP311.13

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