發(fā)布時(shí)間：2018-05-28 03:41

  本文選題：因果網(wǎng)絡(luò) + 因果推斷��； 參考：《廣東工業(yè)大學(xué)》2016年碩士論文

【摘要】：隨著社交網(wǎng)絡(luò)的飛速發(fā)展,越來越多的人開始挖掘社交網(wǎng)絡(luò)潛在的價(jià)值,進(jìn)而推動(dòng)相關(guān)產(chǎn)業(yè)的發(fā)展,例如微商、微博營(yíng)銷、社交化電商等。在社交網(wǎng)絡(luò)眾多相關(guān)研究中,用戶影響力對(duì)輿論引導(dǎo)、微博營(yíng)銷具有現(xiàn)實(shí)意義,是當(dāng)前研究的難點(diǎn)和熱點(diǎn)�，F(xiàn)有的研究用戶影響力的方法主要基于用戶顯式聲明的好友網(wǎng)絡(luò),然而用戶顯式聲明的好友網(wǎng)絡(luò)往往具有較大的冗余性。具體表現(xiàn)為大量顯式聲明的好友網(wǎng)絡(luò)對(duì)于用戶的影響力沒有實(shí)質(zhì)作用。因此,如何基于用戶行為數(shù)據(jù),挖掘用戶行為之間的因果網(wǎng)絡(luò)是用戶影響力評(píng)估的關(guān)鍵,具有重要的意義。然而,已有的社交網(wǎng)絡(luò)因果關(guān)系推斷方法中存在兩個(gè)問題：1.無法識(shí)別間接因果影響而導(dǎo)致因果網(wǎng)絡(luò)出現(xiàn)大量冗余邊；2.沒有充分考慮因果影響滯后長(zhǎng)度。本文基于最小描述長(zhǎng)度準(zhǔn)則(MDL)對(duì)上述兩個(gè)問題進(jìn)行了統(tǒng)一建模,提出了一種新的模型MCRNC Minimal Causal Network)。在減少因果網(wǎng)絡(luò)冗余方面,MCRN模型將因果傳遞熵算法應(yīng)用于社交網(wǎng)絡(luò)因果關(guān)系發(fā)現(xiàn)上,同時(shí)結(jié)合因果影響滯后長(zhǎng)度對(duì)因果傳遞熵進(jìn)行拓展,上述策略有效剔除了結(jié)構(gòu)中的冗余邊,提高算法的精確率；在探索因果影響滯后長(zhǎng)度方面,使用MDL作為模型的評(píng)分標(biāo)準(zhǔn),權(quán)衡模型的不確性和復(fù)雜度,有效降低了模型過擬合的問題。本文通過大量模擬數(shù)據(jù)集驗(yàn)證MCRN模型的多個(gè)評(píng)測(cè)指標(biāo)都優(yōu)于傳遞熵,因果傳遞熵等類似算法。通過新浪微博真實(shí)數(shù)據(jù)集的實(shí)驗(yàn)發(fā)現(xiàn)用戶顯式聲明的好友關(guān)系很多不存在因果影響,存在因果關(guān)系的用戶之間存在互動(dòng)行為等現(xiàn)象,較好地驗(yàn)證了本模型的有效性。最后,本文以MCRN模型為理論基礎(chǔ),提出一個(gè)基于時(shí)序的社交網(wǎng)絡(luò)因果關(guān)系發(fā)現(xiàn)系統(tǒng)的構(gòu)建方案,簡(jiǎn)稱MCRN系統(tǒng),并給出系統(tǒng)架構(gòu)和系統(tǒng)原型,該系統(tǒng)有助于用戶準(zhǔn)確直觀地分析用戶之間的因果關(guān)系,并進(jìn)一步應(yīng)用于現(xiàn)實(shí)生活中的其他領(lǐng)域。
[Abstract]:With the rapid development of social networks, more and more people begin to tap the potential value of social networks, and then promote the development of related industries, such as micro-quotient, Weibo marketing, social e-commerce and so on. In many related researches of social network, user influence has practical significance to guide public opinion and Weibo marketing, which is the difficulty and hot spot of current research. The existing methods to study user's influence are mainly based on the user's explicitly declared friend network, however, the user's explicitly declared friend network is often redundant. It shows that a large number of explicit friend networks have no real impact on users. Therefore, how to mine causal networks between user behaviors based on user behavior data is the key of user impact assessment and has important significance. However, there are two problems in the existing causality inference methods of social networks: 1. The failure to identify indirect causal effects leads to a large number of redundant side effects in causal networks. The lag length of causality is not fully taken into account. This paper presents a unified modeling of the two problems based on the minimum description length criterion (MDL), and proposes a new model, MCRNC Minimal Causal Network. In the aspect of reducing redundancy of causality network, MCRN model applies causality transfer entropy algorithm to the discovery of causality in social network, and extends causality transfer entropy by combining the length of causality influence lag. The above strategy can effectively eliminate redundant edges in the structure. In order to improve the accuracy rate of the algorithm and to explore the delay length of causality, MDL is used as the scoring criterion of the model to weigh the uncertainty and complexity of the model, and the problem of model overfitting is effectively reduced. In this paper, a large number of simulated data sets are used to verify that many evaluation indexes of MCRN model are superior to similar algorithms such as transfer entropy, causal transfer entropy and so on. Through the experiments of the real data set of Sina Weibo, it is found that there is no causality in many of the user's declared friendships, and there are some phenomena such as interactive behavior among the users who have the causal relationship. The validity of this model is well verified. Finally, based on the MCRN model, this paper proposes a time-series based causality discovery system for social networks, called MCRN system, and gives the architecture and prototype of the system. The system can help users analyze the causality between users accurately and intuitively, and further apply it to other fields in real life.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09

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