本文選題：社會(huì)化網(wǎng)絡(luò) + 網(wǎng)絡(luò)群體行為��； 參考：《北京郵電大學(xué)》2014年博士論文

【摘要】：隨著信息技術(shù)的發(fā)展,尤其是云計(jì)算、物聯(lián)網(wǎng)、社會(huì)化網(wǎng)絡(luò)以及信息獲取技術(shù)的進(jìn)步,人類逐漸步入大數(shù)據(jù)時(shí)代。數(shù)據(jù)規(guī)模的飛速增長,促使了結(jié)合計(jì)算科學(xué)和社會(huì)科學(xué)的交叉學(xué)科：社會(huì)計(jì)算的誕生。 社會(huì)計(jì)算旨在利用計(jì)算機(jī)技術(shù)和社科理論搭建虛擬網(wǎng)絡(luò)與現(xiàn)實(shí)社會(huì)之間的橋梁,通過對(duì)網(wǎng)絡(luò)化數(shù)據(jù)的分析揭示網(wǎng)絡(luò)群體的交互規(guī)律,幫助人們認(rèn)識(shí)和研究社會(huì)科學(xué)的各種問題。本文圍繞社會(huì)計(jì)算中的若干關(guān)鍵問題,從社會(huì)化網(wǎng)絡(luò)的群體互動(dòng)規(guī)律、網(wǎng)絡(luò)虛擬社區(qū)發(fā)現(xiàn)和群體輿論的聚集機(jī)理三個(gè)方面入手,研究相關(guān)算法和模型,其主要?jiǎng)?chuàng)新性工作概括如下： 1.針對(duì)社會(huì)化網(wǎng)絡(luò)中的即時(shí)通信社區(qū)群體特點(diǎn),分析微博社區(qū)與即時(shí)通信社區(qū)在傳播范圍、隱私性、實(shí)時(shí)性、用戶參與度和會(huì)話特性共五方面的異同。通過對(duì)即時(shí)通信社區(qū)和微博社區(qū)的用戶行為進(jìn)行分析,提出群體互動(dòng)指數(shù)Sj-inf和群體惰性Iinert兩個(gè)驅(qū)動(dòng)用戶行為的動(dòng)力學(xué)指標(biāo),并在此基礎(chǔ)上,提出一種基于興趣與群體互動(dòng)驅(qū)動(dòng)的行為模型ICHM。模型考慮了群體互動(dòng)指數(shù)、群體惰性和個(gè)體興趣三個(gè)影響用戶行為的動(dòng)力學(xué)因素。實(shí)驗(yàn)結(jié)果表明,ICHM生成的群體信息發(fā)布行為的時(shí)間間隔服從單一指數(shù)的冪律分布。通過參數(shù)調(diào)整可以得到與真實(shí)數(shù)據(jù)相近的冪律分布特性,與實(shí)際數(shù)據(jù)表現(xiàn)的動(dòng)力學(xué)特征相吻合。能夠?yàn)榧磿r(shí)通信社區(qū)的群體行為特征提供合理有效的解釋。 2.針對(duì)傳統(tǒng)人類動(dòng)力學(xué)實(shí)證結(jié)果中出現(xiàn)的指數(shù)截?cái)嗵匦?結(jié)合即時(shí)通信社區(qū)個(gè)體之間以會(huì)話為中心的交互特性,在ICHM模型的基礎(chǔ)上,進(jìn)一步提出基于會(huì)話驅(qū)動(dòng)的用戶行為模型SICHM。模型基于個(gè)體會(huì)話交互原則,引入會(huì)話轉(zhuǎn)移概率Ptrans和會(huì)話退出概率Pcancel約束個(gè)體的信息發(fā)布行為。實(shí)驗(yàn)結(jié)果表明,在會(huì)話驅(qū)動(dòng)的交互行為中,個(gè)體信息發(fā)布的時(shí)間間隔服從帶有指數(shù)截?cái)嗟膬缏煞植?用戶會(huì)話轉(zhuǎn)移概率Ptrans在一定區(qū)間內(nèi)會(huì)影響兩段冪率的冪指數(shù)和截?cái)辔?當(dāng)Ptrans偏離該區(qū)間范圍,個(gè)體行為可以用單一冪指數(shù)的冪函數(shù)刻畫。通過參數(shù)調(diào)整,模型可以生成與真實(shí)數(shù)據(jù)集一致的帶有指數(shù)截?cái)嗟膬缏煞植?表明會(huì)話驅(qū)動(dòng)的特性是導(dǎo)致人類行為的冪律特性產(chǎn)生指數(shù)截?cái)嗟脑蛑?3.針對(duì)傳統(tǒng)社區(qū)發(fā)現(xiàn)算法的效率問題,提出一種適用于有向網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)的網(wǎng)絡(luò)稀疏化算法。算法基于鄰居節(jié)點(diǎn)之間的共引、傳遞和耦合三種關(guān)系計(jì)算其歸一化相似度,并引入Minwise哈希函數(shù)提高相似度計(jì)算的效率。在此基礎(chǔ)上,提出基于局部相似度的網(wǎng)絡(luò)稀疏化算法LSDN。算法充分考慮網(wǎng)絡(luò)的局部特性,使得網(wǎng)絡(luò)在稀疏前后的入度和出度均服從冪律分布,從而保證網(wǎng)絡(luò)稀疏前后的整體分布特性。實(shí)驗(yàn)結(jié)果表明,所提出的網(wǎng)絡(luò)稀疏算法可以有效地對(duì)有向網(wǎng)絡(luò)進(jìn)行簡化,能夠在不改變網(wǎng)絡(luò)整體特性的同時(shí)保證社區(qū)發(fā)現(xiàn)的準(zhǔn)確度,從而提升社區(qū)發(fā)現(xiàn)的效率,解決了傳統(tǒng)社區(qū)發(fā)現(xiàn)算法在可擴(kuò)展性方面考慮不足和效率不高的問題。 4.針對(duì)傳統(tǒng)輿論演化模型忽略群體一致性壓力,缺乏個(gè)體從眾效應(yīng)下決策行為研究的問題,分析網(wǎng)絡(luò)群體輿論演化的驅(qū)動(dòng)力,提出—種基于決策偏移的輿論演化動(dòng)力學(xué)模型DO2M。模型基于社會(huì)心理學(xué)的從眾效應(yīng),引入群體一致性壓力和個(gè)期望牽引力,建立依從、趨同和內(nèi)化三種不同節(jié)點(diǎn)的狀態(tài)轉(zhuǎn)移策略和觀點(diǎn)演化策略。同時(shí),基于決策偏移思想在經(jīng)典有限信任模型HK模型上進(jìn)行改進(jìn),構(gòu)建HK-DO2M模型。實(shí)驗(yàn)結(jié)果表明,提出的兩個(gè)模型均能夠有效模擬群體輿論演化的收斂和分化,與經(jīng)典有限信任模型相比,模型將個(gè)體的期望觀點(diǎn)和實(shí)際觀點(diǎn)分離,其觀點(diǎn)偏移的特性更加符合社會(huì)網(wǎng)絡(luò)中群體輿論演進(jìn)和個(gè)體交互的行為特征,揭示了群體層面的觀點(diǎn)演化的內(nèi)在規(guī)律,為大數(shù)據(jù)時(shí)代分析現(xiàn)實(shí)輿論形成的內(nèi)在機(jī)理提供理論模型和參考。
[Abstract]:With the development of information technology, especially the progress of cloud computing, Internet of things, social networks and information acquisition technology, human beings have gradually stepped into the era of big data. The rapid growth of data scale has prompted the interdisciplinary science and social sciences to be combined: the birth of social computing.
Social computing aims to build a bridge between virtual network and real society by using computer technology and social science theory. Through the analysis of network data, it reveals the interaction rules of network groups and helps people to understand and study the various problems of social science. This paper focuses on some key problems in social computing, from the group of social networks. There are three aspects of the law of body interaction, the discovery of network virtual community and the aggregation mechanism of group public opinion, and the related algorithms and models are studied. The main innovative work is as follows:
1. to analyze the similarities and differences between the micro-blog community and the instant communication community in the five aspects of the communication range, privacy, real time, user participation and conversation characteristics in the social network, and analyze the user behavior of the instant communication community and the micro-blog community, and propose the group interaction index Sj-inf and the group laziness. On the basis of the two dynamic indicators that drive user behavior, a behavioral model ICHM. model based on interest and group interaction is proposed, and three dynamic factors affecting user behavior are considered, including group interaction index, group inertia and individual interest. Experimental results show that the group information release behavior generated by ICHM is generated by ICHM. The time interval obeys the power law distribution of the single exponent. The power law distribution similar to the real data can be obtained by the parameter adjustment, which is consistent with the dynamic characteristics of the actual data. It can provide a reasonable and effective explanation for the community behavior characteristics of the instant communication community.
2. on the basis of ICHM model, the SICHM. model of user behavior model based on session driven based on the interaction principle of experience language, and introducing the session transfer probability Ptrans, and the interactive characteristics of the traditional human dynamics empirical results, combined with the conversation centered interaction between the individuals in the instant communication community. The session exit probability Pcancel restricts the information release behavior of the individual. The experimental results show that, in the session driven interaction, the time interval of individual information release obeys exponential truncated power law distribution, and the user session transfer probability Ptrans affects the power exponent and the cutoff of the two power exponents in a certain interval, when Ptrans deviates from that Interval range, individual behavior can be portrayed by a power function of a single power exponent. By adjusting the parameters, the model can generate an exponential truncated power law distribution consistent with the real dataset, indicating that the characteristic of session driven is the reason why the power law characteristic of human behavior is truncated exponentially.
3. in view of the efficiency of the traditional community discovery algorithm, a network sparsity algorithm is proposed for the discovery of a directed network community. The algorithm calculates its normalized similarity based on the three relationships of the co citation, transfer and coupling between the neighbor nodes, and introduces the Minwise hash function to improve the efficiency of the similarity calculation. On this basis, the base is proposed. The local similarity based network sparsation algorithm LSDN. takes full consideration of the local characteristics of the network, which makes the admission and output of the network obey the power law distribution before and after the sparsity, so as to ensure the overall distribution of the network before and after the sparse network. The experimental results show that the proposed network sparse algorithm can effectively simplify the directed network. It can ensure the accuracy of community discovery without changing the overall characteristics of the network, so as to improve the efficiency of community discovery, and solve the problem that the traditional community discovery algorithm is insufficient and inefficient in the aspect of extensibility.
4. the traditional opinion evolution model ignores the group consistency pressure, lacks the problem of the decision behavior research under the individual herd effect, analyzes the driving force of the network group's public opinion evolution, and proposes a public opinion evolution dynamic model DO2M. model based on the decision shift, which is based on the herd effect of the sociopsycho psychology, and introduces the group consistency pressure and the one. We expect the traction force to establish the state transfer strategy and the viewpoint evolution strategy of three different nodes, including compliance, convergence and internalization. At the same time, based on the decision migration idea, the HK-DO2M model is constructed on the classic finite trust model HK model. The experimental results show that the proposed two models can effectively simulate the convergence and convergence of the group opinion evolution. Differentiation, compared with the classic finite trust model, the model separations the individual's expectation from the actual point of view. The characteristic of the view offset is more consistent with the evolution of the public opinion and the behavior characteristics of the individual interaction in the social network, reveals the inherent law of the view evolution of the group level, and is the inner machine for the analysis of the reality public opinion in the large data age. The theory provides a theoretical model and reference.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.01

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