本文選題：網絡數據 + 鏈路預測��； 參考：《電子科技大學》2017年博士論文

【摘要】：大數據時代,數據通過“量化一切”形成數據世界。由于數據是世界的客觀反映,所以數據的分析挖掘工作可以指導人們認識世界、改造世界。隨著信息技術的發(fā)展普及,社會和企業(yè)都產生了海量的數據資源,需要被分析利用。同時,網絡化是現實世界的普遍特征和內在規(guī)律,自然元素、物種人群等各種對象元素相互影響、相互依賴形成網絡系統。由于數據產生的客觀性和普遍性,數據世界中的數據資源基本上都是刻畫網絡化現實世界特征規(guī)律的網絡化數據。另外,由于數據產生的弱約束性以及強覆蓋性,收集的數據資源在客觀、準確刻畫現實世界的同時,具有多源多態(tài)、復雜異構特征。所以,當前數據處理的主要對象為海量的復雜異構網絡數據。新型的復雜異構網絡數據對傳統數據處理技術產生了巨大的挑戰(zhàn)。為了分析挖掘新型的復雜異構網絡數據,本文探索研究基于數據特征的、面向現實需求的新型數據處理理論和模型。復雜異構網絡數據主要包括網絡結構數據、網絡行為數據以及網絡內容數據,本文從不用角度、不同需求、不同方法對復雜網絡數據進行模式挖掘和演化分析研究,凝練復雜網絡數據處理的研究范式和計算框架,探索復雜網絡數據蘊含的科學問題、問題相關數據的特征規(guī)律以及問題的求解方案,構建復雜網絡數據處理的技術體系。具體研究內容和創(chuàng)新點包括:1.基于標記傳播的網絡結構模式整體檢測分析算法針對復雜異構的網絡拓撲,以社團結構為主體、同時考慮網絡節(jié)點的不同角色進行多尺度、多層次網絡結構模式的挖掘研究,提出一個基于標記傳播過程的網絡結構模式發(fā)現算法LINSIA。LINSIA通過允許節(jié)點同時擁有不同的網絡標記從而能夠識別樞紐節(jié)點和重疊社團,通過構建多層次網絡結構樹并進行最優(yōu)層次分割從而發(fā)現網絡的多層次、多尺度結構模式,通過標記選擇和標記更新策略的創(chuàng)新提出與網絡異構程度相適應的標記傳播過程,從而發(fā)現離群節(jié)點、避免極大社團。實驗結果表明LINSIA算法性能良好,其關于網絡結構模式挖掘的綜合性解決方案對網絡結構數據的分析研究工作具有重要的理論意義。2.面向最優(yōu)網絡分裂的節(jié)點中心性度量方法本文面向最優(yōu)網絡分裂問題,從微觀角度探索網絡的結構和功能特征,提出基于鄰居節(jié)點度信息熵和本地結構聚類密度的ECI節(jié)點中心性。實驗結果表明,ECI中心性在網絡分裂過程中性能明顯優(yōu)于傳統的CI中心性。同時,基于局部結構信息的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中心性的性能表現和網絡結構特征之間的關聯關系,對ECI中心性的適用范圍進行討論,為最優(yōu)網絡分裂問題中的節(jié)點中心性選擇提供指導。另外,通過借鑒物質傳播和熱傳導物理過程,本文在迭代更新框架中定義非線性混合更新機制,從而提出PIRank節(jié)點中心性。該中心性整合物質傳播和熱傳導過程對網絡重要節(jié)點的不同偏好,能夠發(fā)現具有不同特征的網絡重要節(jié)點。實驗結果表明,PIRank節(jié)點中心性對最優(yōu)網絡分裂問題性能表現良好。3.基于節(jié)點位置漂移模型的動態(tài)網絡演化預測算法針對動態(tài)演化網絡,提出一種結合節(jié)點位置漂移模型和鏈路預測方法的網絡演化預測算法。此工作首先提出以網絡平均最短距離為指導的相似性度量WSD。然后,基于動態(tài)演化網絡的聚集特性和時效特性定義鄰居節(jié)點對中心節(jié)點的時空影響力,并以引力場的視角比較鄰居節(jié)點的時空影響力強度和本地網絡的固有結構強度,從而提出更新中心節(jié)點網絡位置的時空漂移模型。算法基于此漂移模型推理動態(tài)網絡未來的結構狀態(tài),并基于未來的網絡結構狀態(tài)預測未來的網絡鏈路。實驗結果表明,本文提出的相似性度量WSD與其它經典方法相比性能更優(yōu),結合位置漂移模型能夠準確預測網絡演化。4.基于個體轉發(fā)行為建模的在線社交網絡信息傳播演化預測方法針對信息傳播過程,提出基于微觀個體轉發(fā)行為估計的多尺度信息傳播預測方法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體轉發(fā)行為集合以及承載轉發(fā)行為的網絡拓撲結構。對于微觀個體轉發(fā)行為,MScaleDP從多個維度構建轉發(fā)特征,并以二分類模型進行建模。MScaleDP考慮信息級聯傳播與標記傳播方法LPA的內在一致性,以微觀個體轉發(fā)模型替代LPA的標記更新機制,并通過對LPA傳播過程進行限制提出了 AULPA級聯傳播預測算法。實驗結果表明結合個體轉發(fā)行為估計模型和AULPA級聯傳播預測算法,MScaleDP能夠全面、準確的預測信息傳播,性能優(yōu)于傳統方法。本文還對影響信息傳播的主要驅動機制進行了挖掘分析,發(fā)現時效特征和內容特征是信息傳播的主要影響因素。綜上,本文圍繞復雜網絡數據的模式挖掘和演化分析展開了研究,針對四個方面的問題提出了解決方案,并進行了大量的實驗驗證。實驗結果表明,本文發(fā)現的特征規(guī)律以及提出的模型算法準確有效、性能優(yōu)良。本文工作成果不僅具有重要的理論意義,也具有廣泛的實際應用價值。
[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.
【學位授予單位】：電子科技大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：O157.5

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相關期刊論文 前1條

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本文編號：2068334