本文選題：復(fù)雜網(wǎng)絡(luò) + 社交網(wǎng)絡(luò)��； 參考：《北京郵電大學(xué)》2016年博士論文

【摘要】：隨著Web2.0和移動(dòng)互聯(lián)網(wǎng)技術(shù)的快速發(fā)展和成熟,以Twitter、Facebook、新浪微博等為代表的社交網(wǎng)絡(luò)已逐漸融入到人們的日常生活中。社交網(wǎng)絡(luò)根據(jù)用戶不同的功能訴求,將社會各個(gè)層次的特定用戶聚集在一起,使得空間差異、時(shí)間差異等因素不再成為人們交流的障礙,實(shí)現(xiàn)了社會關(guān)系在虛擬網(wǎng)絡(luò)上的延伸。隨著社交網(wǎng)絡(luò)的蓬勃發(fā)展,信息在社交網(wǎng)絡(luò)中的傳播變得更加普適和廣泛,海量的用戶可以方便的在社交網(wǎng)絡(luò)中瀏覽新聞、關(guān)注熱點(diǎn)、與好友或陌生人互動(dòng),每時(shí)每刻都可以通過評論、轉(zhuǎn)發(fā)、發(fā)帖等行為表達(dá)自己多樣的情感。雖然網(wǎng)絡(luò)中的交流經(jīng)常被認(rèn)為是虛擬的,但情感會隨著信息在網(wǎng)絡(luò)中傳播擴(kuò)散,產(chǎn)生各種人與人之間的情感互動(dòng),甚至影響著網(wǎng)絡(luò)用戶在真實(shí)世界的行為表現(xiàn)。人類情感行為的研究一直以來都吸引著來自社會學(xué)、心理學(xué)、經(jīng)濟(jì)學(xué)、計(jì)算機(jī)科學(xué)等多個(gè)學(xué)科研究者們的興趣,但由于人類情感的復(fù)雜性,研究者們也面臨著種種挑戰(zhàn)。社交網(wǎng)絡(luò)中海量用戶行為數(shù)據(jù)被實(shí)時(shí)記錄,給了我們前所未有的研究人類情感行為的機(jī)會。社交網(wǎng)絡(luò)用戶群體的情感行為研究具有廣泛的應(yīng)用基礎(chǔ)和重要的現(xiàn)實(shí)意義。本文以社交網(wǎng)絡(luò)為研究對象,利用復(fù)雜網(wǎng)絡(luò)理論和數(shù)據(jù)挖掘方法,對網(wǎng)絡(luò)用戶群體情感行為所涉及的若干關(guān)鍵問題進(jìn)行初步探索和研究。主要研究內(nèi)容包括:'用戶分級別情感行為的分析建模仿真'、'基于多元情感的用戶聚類分析'、'用戶情感影響者發(fā)現(xiàn)模型'、'用戶情感社團(tuán)發(fā)現(xiàn)'。論文的主要工作和創(chuàng)新點(diǎn)如下:(1)基于新浪微博數(shù)據(jù),提出了社交網(wǎng)絡(luò)用戶分級別情感發(fā)帖模型,并通過仿真驗(yàn)證了模型有效性。具體過程為:首先對用戶微博內(nèi)容情感進(jìn)行分級,分析微博用戶群體情感行為。分析發(fā)現(xiàn)社交網(wǎng)絡(luò)用戶群體在表達(dá)某一級別情感的發(fā)帖量均服從冪律分布,且冪指數(shù)隨著情感級別趨向平和而增加,大部分用戶通過微博表達(dá)情感時(shí)較為平和,需要表達(dá)激烈情感時(shí),用戶參與比例會減小。然后建立用戶分級別情感發(fā)帖模型,該模型考慮了發(fā)帖用戶受到周圍情感環(huán)境因素的影響,以及自身情感的隨機(jī)性變化。最后模型仿真驗(yàn)證了網(wǎng)絡(luò)用戶群體分級別情感發(fā)帖量服從冪律分布以及冪指數(shù)的變化趨勢。(2)基于對社交網(wǎng)絡(luò)用戶多元情感行為的分析,提出了一種針對用戶多元情感時(shí)間序列的相似性度量方法,并利用該方法對用戶群體進(jìn)行情感聚類分析。具體過程如下:首先利用多元情感詞庫提取出用戶微博的多元情感向量,并構(gòu)建多元情感時(shí)間序列用以描述用戶情感行為。然后結(jié)合PCA相似性和距離相似性度量用戶間的多元情感行為相似性,該度量既考慮了用戶的情感波動(dòng),又考慮了情感表達(dá)強(qiáng)度。最后將該度量與經(jīng)典的k-means聚類算法結(jié)合,提出多元情感聚類方法,并使用該方法發(fā)現(xiàn)不同用戶情感群體,描述不同群體的情感行為特點(diǎn)。(3)基于社交網(wǎng)絡(luò)的異質(zhì)特點(diǎn)和網(wǎng)絡(luò)用戶間情感互動(dòng),提出了一種微博用戶的情感影響者發(fā)現(xiàn)模型(EmotionRank)。具體過程如下:首先建立包含兩種節(jié)點(diǎn)(用戶、微博)和三種關(guān)系(轉(zhuǎn)發(fā)、關(guān)注、發(fā)帖)的異質(zhì)微博網(wǎng)絡(luò),然后利用微博情感相似性和用戶多元情感行為相似性驗(yàn)證所構(gòu)建網(wǎng)絡(luò)的情感同配性,確認(rèn)情感影響在該網(wǎng)絡(luò)中存在。再利用兩種相似性將該網(wǎng)絡(luò)轉(zhuǎn)化為只包含用戶節(jié)點(diǎn)的同質(zhì)網(wǎng)絡(luò),進(jìn)而在網(wǎng)絡(luò)中使用隨機(jī)游走模型發(fā)現(xiàn)情感影響者。最后基于微博數(shù)據(jù)實(shí)驗(yàn)確認(rèn)了該模型的有效性和優(yōu)越性。(4)基于社交網(wǎng)絡(luò)用戶群體的情感同配性,可以確認(rèn)網(wǎng)絡(luò)用戶會依據(jù)情感行為相似而鏈接聚集形成社團(tuán)。本工作以社交網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)為基礎(chǔ),提出構(gòu)建了以關(guān)注用戶間以及轉(zhuǎn)發(fā)微博間的情感相似性為邊權(quán)重的情感網(wǎng)絡(luò)模型,再利用CNM和BGLL兩種方法在用戶情感網(wǎng)絡(luò)中發(fā)現(xiàn)情感社團(tuán)。為驗(yàn)證情感網(wǎng)絡(luò)更適合發(fā)現(xiàn)情感社團(tuán),情感網(wǎng)絡(luò)與利用其它三種網(wǎng)絡(luò)節(jié)點(diǎn)相似性構(gòu)建的三個(gè)無向有權(quán)網(wǎng)絡(luò)以及一個(gè)無向無權(quán)網(wǎng)絡(luò)進(jìn)行了對比,情感網(wǎng)絡(luò)與四個(gè)對比網(wǎng)絡(luò)有著相同的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)和不同的邊權(quán)重。對比實(shí)驗(yàn)結(jié)果表明利用情感網(wǎng)絡(luò)所發(fā)現(xiàn)的社團(tuán)內(nèi)部用戶之間的情感行為更加相似,用戶間的轉(zhuǎn)發(fā)微博有著更相近的情感。
[Abstract]:With the rapid development of Web2.0 and mobile Internet technology and mature, with Twitter, Facebook, Sina, micro-blog and other social networks have been gradually integrated into people's daily life. The social network according to the functional demands of different users, the specific users of all levels of society together, making the space differences, differences and other factors are no longer time people become obstacles in communication, realize the extension of social relations in the virtual network. With the rapid development of the social network, the spread of information in social networks become more pervasive and widespread, massive users can easily browse news in social networks focus, interact with friends or strangers, all the time through comment, forwarding, posting and other actions to express their feelings. Although the diversity in the network communication is often considered to be virtual, but the emotion with information on the net The network spread, emotional interaction between people and people, and even affect the performance of network users in the real world. The research of human emotional behavior has been drawn from sociology, psychology, economics, computer science and other disciplines researchers' interest, but because of the complexity of human emotions. The researchers also faced various challenges. Massive user behavior in social network data is recorded in real time, to study the behavior of the human emotions we hitherto unknown opportunities. Which has been widely used and important practical significance to research the emotional behavior of social network user groups. This paper takes the social network as the research object, using complex network theory and the data mining method, the preliminary exploration and Research on some key problems related to Internet users emotional behavior. The main contents include: users' Modeling and simulation analysis of 'level of emotional behavior,' analysis' multi user clustering based on emotion, emotional impact that users' model ',' user emotional associations found. Main work and innovations of the thesis are as follows: (1) Sina micro-blog data based on the proposed social network user level emotional post model. And the model is verified by simulation. The specific process is as follows: first, the classification of users of micro-blog micro-blog users group analysis of the emotional content, emotional behavior. The analysis found that the social network user groups are power-law distributions in the amount of post express a level of emotion, and the power exponent with the trend of peace and increase the emotional level, the majority of users micro-blog through the expression of feelings is relatively flat, need to express strong emotions, user participation ratio will be reduced. Then the establishment of user level emotional post model, the model considers the user posting Affected by the emotions surrounding environmental factors, as well as their own emotional random changes. Finally simulation model to verify the change trend of the network user group level emotion posting power-law and exponential. (2) analysis of the social network based on multi user emotional behavior, proposes a method for similarity measurement multi user emotional time series, and the emotion of the clustering analysis of user groups by using this method. The specific process is as follows: firstly, using multiple emotion lexicon extracted multiple emotion vector of micro-blog users, and build a multiple time series is used to describe user emotional emotional behavior. Then combined with PCA similarity and distance similarity measure multiple user emotional behavior the similarity between the measurement of both emotions of the user, and consider the emotional expression intensity. Finally the measure with the classical K-means clustering With the algorithm, put forward multiple emotion clustering method, and used this method to find different user groups emotion, the emotional behavior characteristics of different groups. (3) the emotional interaction between heterogeneous characteristics and network users based on social network, put forward an emotional impact of micro-blog users found model (EmotionRank). The specific process is as follows: first includes the establishment of two kinds of nodes (users, micro-blog) and three relations (forwarding, attention, post) heterogeneous micro-blog network, and then use the micro-blog emotional similarity and multiple user emotional behavior similarity verification construction network emotion homogamety, confirm the emotional effects present in the network. Then two kinds of similar will the network into a user node contains only homogeneous network, and then using the random walk model found that the emotional impact in the network. Finally, based on the experimental data of micro-blog confirmed the validity of the model And superiority. (4) social network user groups based on emotional homogamety, can confirm the network users will be based on emotional behavior is similar and link together to form a club. The work is based on the social network topology, put forward to concern among users and forwarding micro-blog emotion between similarity of emotional edge weight network model then, using CNM and BGLL two ways to find emotional associations in the emotion of the user network. In order to verify the emotional network more suitable for emotional associations, emotional network and the use of the other three kind of network node similarity constructed three undirected weighted network and an undirected unweighted network compared with emotional network the network topology of the same and different edge weights and four contrast network. Experimental results show that more similar emotional behavior between the community users have discovered by using the emotion of the network, with the The inter - Household forwarding micro-blog has a more similar emotion.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP393.09;TP391.1

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