本文關(guān)鍵詞： 社交網(wǎng)絡(luò) 數(shù)據(jù)可視化 社區(qū)發(fā)現(xiàn) 力導(dǎo)引布局 屬性圖聚類　出處：《重慶郵電大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著信息科技的飛速發(fā)展,具有多維和海量等特性的社交網(wǎng)絡(luò)數(shù)據(jù)呈現(xiàn)出爆炸式增長,研究多維海量社交網(wǎng)絡(luò)數(shù)據(jù)的可視化技術(shù)具有重要意義。在分析多維和海量數(shù)據(jù)可視化技術(shù)中存在的主要問題的基礎(chǔ)上,本文對(duì)海量社交網(wǎng)絡(luò)數(shù)據(jù)可視化技術(shù)中的社區(qū)發(fā)現(xiàn)算法、力導(dǎo)引布局算法和多維數(shù)據(jù)可視化中的屬性圖聚類算法進(jìn)行了重點(diǎn)研究。第一,針對(duì)現(xiàn)有社區(qū)發(fā)現(xiàn)算法存在社區(qū)質(zhì)量不滿足圖可視化要求和算法效率低的問題,以及力導(dǎo)引布局算法存在社區(qū)結(jié)構(gòu)不明顯和布局效率低的問題,提出了改進(jìn)的社區(qū)發(fā)現(xiàn)算法和社區(qū)布局算法。首先,基于Louvain算法,提出了一種面向大規(guī)模社交網(wǎng)絡(luò)的社區(qū)發(fā)現(xiàn)算法。該算法結(jié)合社交網(wǎng)絡(luò)中無尺度及小世界等特性,通過預(yù)先選取種子節(jié)點(diǎn)的方法,抑制了大社區(qū)的過度合并,同時(shí)也及時(shí)合并小的社區(qū)。其次,提出了一種展示大規(guī)模網(wǎng)絡(luò)社區(qū)結(jié)構(gòu)的社區(qū)布局算法。該算法通過引入社區(qū)引力促使同一社區(qū)中的節(jié)點(diǎn)聚攏,優(yōu)化了社區(qū)引力建模,簡(jiǎn)化了算法步驟。實(shí)驗(yàn)結(jié)果表明,以上算法能夠清晰、高效地展示海量社交網(wǎng)絡(luò)數(shù)據(jù)。第二,針對(duì)屬性圖聚類算法存在社區(qū)質(zhì)量不滿足圖可視化要求、算法效率低和具有維度災(zāi)難以及人為干預(yù)的問題,提出了改進(jìn)的屬性圖聚類算法和基于屬性映射的多維數(shù)據(jù)可視化算法。其中,改進(jìn)的屬性圖聚類算法采用主成分分析(Principal Component Analysis,PCA)和自組織映射(Self-Organizing Map,SOM)算法分別降低節(jié)點(diǎn)信息維度來完成聚類。然后依據(jù)聚類結(jié)果,利用屬性相似度將原有的無權(quán)網(wǎng)絡(luò)圖轉(zhuǎn)換為加權(quán)網(wǎng)絡(luò)圖,之后利用本文改進(jìn)的社區(qū)發(fā)現(xiàn)算法進(jìn)行社區(qū)劃分。基于屬性映射的多維可視化算法以社區(qū)的角度,通過平行坐標(biāo)系的方式展示數(shù)據(jù)的維度信息。以上算法具有自適應(yīng)性和無監(jiān)督性,能夠滿足大規(guī)模多維社交網(wǎng)絡(luò)對(duì)于社區(qū)劃分和可視化的要求。第三,通過集成上述改進(jìn)的算法,設(shè)計(jì)了多維海量社交網(wǎng)絡(luò)數(shù)據(jù)可視化方案,同時(shí)開發(fā)了一款可視化原型系統(tǒng)。
[Abstract]:With the rapid development of information technology, the social network data with multi-dimensional and magnanimous characteristics has explosive growth. It is of great significance to study the visualization technology of multidimensional mass social network data. Based on the analysis of the main problems in multidimensional and massive data visualization technology, This paper focuses on community discovery algorithm, force guidance placement algorithm and attribute map clustering algorithm in mass social network data visualization. In view of the existing community discovery algorithms, the community quality does not meet the requirements of graph visualization and the efficiency of the algorithm is low, and the community structure is not obvious and the layout efficiency is low in the force-guided placement algorithm. An improved community discovery algorithm and a community layout algorithm are proposed. Firstly, based on the Louvain algorithm, a community discovery algorithm for large-scale social networks is proposed, which combines the scale-free and small-world characteristics of social networks. By pre-selecting seed nodes, the excessive merging of large communities is restrained, and the small communities are merged in a timely manner. Secondly, In this paper, a community layout algorithm is proposed to show the community structure of a large scale network. The algorithm optimizes the community gravity modeling and simplifies the steps of the algorithm by introducing community gravity to make the nodes in the same community gather together. The experimental results show that, The above algorithms can clearly and efficiently display massive social network data. Secondly, the attribute map clustering algorithm has problems such as community quality does not meet the requirements of graph visualization, low efficiency, dimensionality disaster and human intervention. An improved attribute map clustering algorithm and a multidimensional data visualization algorithm based on attribute mapping are proposed. The improved attribute map clustering algorithm uses principal component analysis (PCA) and self-organizing map (SOM) algorithm to reduce the dimension of node information respectively. The original unauthorized network graph is transformed into a weighted network graph by using attribute similarity, and then the community is divided by using the improved community discovery algorithm in this paper. The multi-dimensional visualization algorithm based on attribute mapping is based on the community perspective. The above algorithms are self-adaptive and unsupervised, and can meet the requirements of community division and visualization in large-scale multi-dimensional social networks. By integrating the above improved algorithms, a multi-dimensional massive social network data visualization scheme is designed, and a visualization prototype system is developed.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09;TP311.13

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本文編號(hào)：1553903