本文選題：網(wǎng)絡(luò)數(shù)據(jù) + 鏈路預(yù)測�。� 參考：《電子科技大學(xué)》2017年博士論文

【摘要】：大數(shù)據(jù)時(shí)代,數(shù)據(jù)通過“量化一切”形成數(shù)據(jù)世界。由于數(shù)據(jù)是世界的客觀反映,所以數(shù)據(jù)的分析挖掘工作可以指導(dǎo)人們認(rèn)識(shí)世界、改造世界。隨著信息技術(shù)的發(fā)展普及,社會(huì)和企業(yè)都產(chǎn)生了海量的數(shù)據(jù)資源,需要被分析利用。同時(shí),網(wǎng)絡(luò)化是現(xiàn)實(shí)世界的普遍特征和內(nèi)在規(guī)律,自然元素、物種人群等各種對象元素相互影響、相互依賴形成網(wǎng)絡(luò)系統(tǒng)。由于數(shù)據(jù)產(chǎn)生的客觀性和普遍性,數(shù)據(jù)世界中的數(shù)據(jù)資源基本上都是刻畫網(wǎng)絡(luò)化現(xiàn)實(shí)世界特征規(guī)律的網(wǎng)絡(luò)化數(shù)據(jù)。另外,由于數(shù)據(jù)產(chǎn)生的弱約束性以及強(qiáng)覆蓋性,收集的數(shù)據(jù)資源在客觀、準(zhǔn)確刻畫現(xiàn)實(shí)世界的同時(shí),具有多源多態(tài)、復(fù)雜異構(gòu)特征。所以,當(dāng)前數(shù)據(jù)處理的主要對象為海量的復(fù)雜異構(gòu)網(wǎng)絡(luò)數(shù)據(jù)。新型的復(fù)雜異構(gòu)網(wǎng)絡(luò)數(shù)據(jù)對傳統(tǒng)數(shù)據(jù)處理技術(shù)產(chǎn)生了巨大的挑戰(zhàn)。為了分析挖掘新型的復(fù)雜異構(gòu)網(wǎng)絡(luò)數(shù)據(jù),本文探索研究基于數(shù)據(jù)特征的、面向現(xiàn)實(shí)需求的新型數(shù)據(jù)處理理論和模型。復(fù)雜異構(gòu)網(wǎng)絡(luò)數(shù)據(jù)主要包括網(wǎng)絡(luò)結(jié)構(gòu)數(shù)據(jù)、網(wǎng)絡(luò)行為數(shù)據(jù)以及網(wǎng)絡(luò)內(nèi)容數(shù)據(jù),本文從不用角度、不同需求、不同方法對復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)進(jìn)行模式挖掘和演化分析研究,凝練復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)處理的研究范式和計(jì)算框架,探索復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)蘊(yùn)含的科學(xué)問題、問題相關(guān)數(shù)據(jù)的特征規(guī)律以及問題的求解方案,構(gòu)建復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)處理的技術(shù)體系。具體研究內(nèi)容和創(chuàng)新點(diǎn)包括:1.基于標(biāo)記傳播的網(wǎng)絡(luò)結(jié)構(gòu)模式整體檢測分析算法針對復(fù)雜異構(gòu)的網(wǎng)絡(luò)拓?fù)?以社團(tuán)結(jié)構(gòu)為主體、同時(shí)考慮網(wǎng)絡(luò)節(jié)點(diǎn)的不同角色進(jìn)行多尺度、多層次網(wǎng)絡(luò)結(jié)構(gòu)模式的挖掘研究,提出一個(gè)基于標(biāo)記傳播過程的網(wǎng)絡(luò)結(jié)構(gòu)模式發(fā)現(xiàn)算法LINSIA。LINSIA通過允許節(jié)點(diǎn)同時(shí)擁有不同的網(wǎng)絡(luò)標(biāo)記從而能夠識(shí)別樞紐節(jié)點(diǎn)和重疊社團(tuán),通過構(gòu)建多層次網(wǎng)絡(luò)結(jié)構(gòu)樹并進(jìn)行最優(yōu)層次分割從而發(fā)現(xiàn)網(wǎng)絡(luò)的多層次、多尺度結(jié)構(gòu)模式,通過標(biāo)記選擇和標(biāo)記更新策略的創(chuàng)新提出與網(wǎng)絡(luò)異構(gòu)程度相適應(yīng)的標(biāo)記傳播過程,從而發(fā)現(xiàn)離群節(jié)點(diǎn)、避免極大社團(tuán)。實(shí)驗(yàn)結(jié)果表明LINSIA算法性能良好,其關(guān)于網(wǎng)絡(luò)結(jié)構(gòu)模式挖掘的綜合性解決方案對網(wǎng)絡(luò)結(jié)構(gòu)數(shù)據(jù)的分析研究工作具有重要的理論意義。2.面向最優(yōu)網(wǎng)絡(luò)分裂的節(jié)點(diǎn)中心性度量方法本文面向最優(yōu)網(wǎng)絡(luò)分裂問題,從微觀角度探索網(wǎng)絡(luò)的結(jié)構(gòu)和功能特征,提出基于鄰居節(jié)點(diǎn)度信息熵和本地結(jié)構(gòu)聚類密度的ECI節(jié)點(diǎn)中心性。實(shí)驗(yàn)結(jié)果表明,ECI中心性在網(wǎng)絡(luò)分裂過程中性能明顯優(yōu)于傳統(tǒng)的CI中心性。同時(shí),基于局部結(jié)構(gòu)信息的ECI中心性取得了媲美全局性方法的分裂效果。本文通過分析ECI中心性的性能表現(xiàn)和網(wǎng)絡(luò)結(jié)構(gòu)特征之間的關(guān)聯(lián)關(guān)系,對ECI中心性的適用范圍進(jìn)行討論,為最優(yōu)網(wǎng)絡(luò)分裂問題中的節(jié)點(diǎn)中心性選擇提供指導(dǎo)。另外,通過借鑒物質(zhì)傳播和熱傳導(dǎo)物理過程,本文在迭代更新框架中定義非線性混合更新機(jī)制,從而提出PIRank節(jié)點(diǎn)中心性。該中心性整合物質(zhì)傳播和熱傳導(dǎo)過程對網(wǎng)絡(luò)重要節(jié)點(diǎn)的不同偏好,能夠發(fā)現(xiàn)具有不同特征的網(wǎng)絡(luò)重要節(jié)點(diǎn)。實(shí)驗(yàn)結(jié)果表明,PIRank節(jié)點(diǎn)中心性對最優(yōu)網(wǎng)絡(luò)分裂問題性能表現(xiàn)良好。3.基于節(jié)點(diǎn)位置漂移模型的動(dòng)態(tài)網(wǎng)絡(luò)演化預(yù)測算法針對動(dòng)態(tài)演化網(wǎng)絡(luò),提出一種結(jié)合節(jié)點(diǎn)位置漂移模型和鏈路預(yù)測方法的網(wǎng)絡(luò)演化預(yù)測算法。此工作首先提出以網(wǎng)絡(luò)平均最短距離為指導(dǎo)的相似性度量WSD。然后,基于動(dòng)態(tài)演化網(wǎng)絡(luò)的聚集特性和時(shí)效特性定義鄰居節(jié)點(diǎn)對中心節(jié)點(diǎn)的時(shí)空影響力,并以引力場的視角比較鄰居節(jié)點(diǎn)的時(shí)空影響力強(qiáng)度和本地網(wǎng)絡(luò)的固有結(jié)構(gòu)強(qiáng)度,從而提出更新中心節(jié)點(diǎn)網(wǎng)絡(luò)位置的時(shí)空漂移模型。算法基于此漂移模型推理動(dòng)態(tài)網(wǎng)絡(luò)未來的結(jié)構(gòu)狀態(tài),并基于未來的網(wǎng)絡(luò)結(jié)構(gòu)狀態(tài)預(yù)測未來的網(wǎng)絡(luò)鏈路。實(shí)驗(yàn)結(jié)果表明,本文提出的相似性度量WSD與其它經(jīng)典方法相比性能更優(yōu),結(jié)合位置漂移模型能夠準(zhǔn)確預(yù)測網(wǎng)絡(luò)演化。4.基于個(gè)體轉(zhuǎn)發(fā)行為建模的在線社交網(wǎng)絡(luò)信息傳播演化預(yù)測方法針對信息傳播過程,提出基于微觀個(gè)體轉(zhuǎn)發(fā)行為估計(jì)的多尺度信息傳播預(yù)測方法MScaleDP。MScaleDP適用于不同規(guī)模的信息傳播過程、不依賴于任何全局信息。MScaleDP將信息傳播過程分解為微觀個(gè)體轉(zhuǎn)發(fā)行為集合以及承載轉(zhuǎn)發(fā)行為的網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)。對于微觀個(gè)體轉(zhuǎn)發(fā)行為,MScaleDP從多個(gè)維度構(gòu)建轉(zhuǎn)發(fā)特征,并以二分類模型進(jìn)行建模。MScaleDP考慮信息級聯(lián)傳播與標(biāo)記傳播方法LPA的內(nèi)在一致性,以微觀個(gè)體轉(zhuǎn)發(fā)模型替代LPA的標(biāo)記更新機(jī)制,并通過對LPA傳播過程進(jìn)行限制提出了 AULPA級聯(lián)傳播預(yù)測算法。實(shí)驗(yàn)結(jié)果表明結(jié)合個(gè)體轉(zhuǎn)發(fā)行為估計(jì)模型和AULPA級聯(lián)傳播預(yù)測算法,MScaleDP能夠全面、準(zhǔn)確的預(yù)測信息傳播,性能優(yōu)于傳統(tǒng)方法。本文還對影響信息傳播的主要驅(qū)動(dòng)機(jī)制進(jìn)行了挖掘分析,發(fā)現(xiàn)時(shí)效特征和內(nèi)容特征是信息傳播的主要影響因素。綜上,本文圍繞復(fù)雜網(wǎng)絡(luò)數(shù)據(jù)的模式挖掘和演化分析展開了研究,針對四個(gè)方面的問題提出了解決方案,并進(jìn)行了大量的實(shí)驗(yàn)驗(yàn)證。實(shí)驗(yàn)結(jié)果表明,本文發(fā)現(xiàn)的特征規(guī)律以及提出的模型算法準(zhǔn)確有效、性能優(yōu)良。本文工作成果不僅具有重要的理論意義,也具有廣泛的實(shí)際應(yīng)用價(jià)值。
[Abstract]:As the data is the objective reflection of the world, data analysis and mining can guide people to know the world and transform the world. As the development and popularization of information technology, the society and enterprises have produced massive data resources and need to be analyzed and utilized. At the same time, the network can be used. It is the universal characteristic and inherent law of the real world. The elements of natural elements, species and other object elements influence each other and form a network system with each other. The data resources in the data world are basically networked data that depict the characteristics of the present world. In addition, because of the objectivity and universality of the data generation, the data resources in the data world are basically network data. The data generated by the weak constraints and strong coverage, the data resources collected are objectively and accurately depicting the real world, with multi source polymorphism and complex isomerism. Therefore, the main object of the current data processing is the massive complex heterogeneous network data. The new complex allosteric network data has produced a huge amount of traditional data processing technology. In order to analyze and excavate new complex heterogeneous network data, this paper explores the new data processing theory and model based on data feature and realistic demand. The data of complex heterogeneous network mainly include network structure data, network behavior data and network volume data. Methods the model mining and evolution analysis of complex network data are carried out. The research paradigm and calculation framework of complex network data processing are condensed. The scientific problems in the complex network data, the characteristics of the related data and the solution of the problems are explored, and the technical system of complex network data processing is constructed. The specific research content is studied. And the innovation points include: 1. the whole detection and analysis algorithm based on the network structure pattern based on the label propagation is based on the complex and heterogeneous network topology, taking the community structure as the main body, taking into account the different roles of the network nodes to carry on the multi scale and multi-level network structure pattern mining, and proposes a network structure model based on the markup propagation process. It is found that LINSIA.LINSIA can identify hub nodes and overlapping communities by allowing nodes to have different network markers at the same time. By constructing a multilevel network structure tree and optimizing hierarchical segmentation, the multi-layer and multi-scale structure pattern of the network is found, and the innovation and network of the label selection and labeling update strategy are proposed. In order to find out the outlier nodes and avoid the great community, the experimental results show that the LINSIA algorithm has good performance. The comprehensive solution of the network structure pattern mining has an important theoretical significance for the analysis and research of the network structure data, and the.2. is facing the optimal network splitting node. The method of heart measurement is oriented to the optimal network splitting problem. The structure and function characteristics of the network are explored from the microscopic point of view. The ECI node centrality based on the neighbor node degree information entropy and the local structure clustering density is proposed. The experimental results show that the performance of ECI centrality is obviously superior to the traditional CI centrality in the network splitting process. The ECI centrality of local structure information has achieved the split effect comparable to that of the global approach. By analyzing the relationship between the performance of the central ECI and the relationship between the network structure features, this paper discusses the applicable scope of the ECI centrality, and provides guidance for the central selection of the nodes in the optimal network splitting problem. In the physical process of mass propagation and heat conduction, this paper defines the nonlinear hybrid update mechanism in the iterative update framework, and proposes the centrality of the PIRank node. This centrality integrates the different preferences of the material propagation and heat conduction process to the important nodes of the network, and can discover the important network nodes with different characteristics. The experimental results show that the PIRank node is used. The performance of the point centrality is good for the optimal network splitting problem. The dynamic network evolution prediction algorithm based on the node position drift model is based on the node position drift model and the network evolution prediction algorithm combining the node position drift model and the link prediction method. The work is first proposed with the network average shortest distance as the guidance. The similarity measure WSD. then defines the spatial and temporal influence of the neighbor nodes on the central nodes based on the aggregation and aging characteristics of the dynamic evolutionary networks, and compares the spatial and temporal intensity of the neighbor nodes with the inherent structural strength of the local networks by the view of the gravitational field, and proposes a spatio-temporal drift model to update the location of the central node network. The algorithm is based on this drift model to inferring the structure state of the future dynamic network and forecast the future network link based on the future network structure state. The experimental results show that the proposed similarity measure WSD is better than other classical methods, and it can predict the network evolution.4. based on individual forwarding accurately with the location drift model. The online social network information propagation evolution prediction method of behavior modeling aims at the information propagation process, and proposes a multi-scale information propagation prediction method based on the estimation of micro individual forwarding behavior, MScaleDP.MScaleDP is suitable for different scale of information propagation process, and does not rely on any global information.MScaleDP to decompose the information propagation process into micro For the micro individual forwarding behavior, MScaleDP constructs the forwarding features from multiple dimensions, and takes the two classification model for modeling.MScaleDP to consider the intrinsic consistency of the information cascade propagation and the markup propagation method LPA, and substitutes the micro individual forwarding model to the standard of LPA. In this paper, the update mechanism is recorded, and the AULPA cascade propagation prediction algorithm is proposed by restricting the LPA propagation process. The experimental results show that combining the individual forwarding behavior estimation model and the AULPA cascade propagation prediction algorithm, the MScaleDP can predict information dissemination accurately and accurately, and the performance is superior to the transmission method. The dynamic mechanism is excavated and analyzed. It is found that the characteristics of time limitation and the characteristics of content are the main influencing factors of information dissemination. In this paper, the paper studies the pattern mining and evolution analysis of complex network data, and puts forward a solution for the four aspects, and has carried out a large number of experimental verification. The experimental results show that this paper finds out the results of this paper. The characteristic law and the proposed model algorithm are accurate and effective, and the performance is excellent. The results of this paper not only have important theoretical significance, but also have extensive practical application value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：O157.5

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相關(guān)期刊論文 前1條

1 ;促進(jìn)大數(shù)據(jù)發(fā)展行動(dòng)綱要[J];成組技術(shù)與生產(chǎn)現(xiàn)代化;2015年03期

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本文編號：2068334