【摘要】：復(fù)雜網(wǎng)絡(luò)系統(tǒng)存在于我們生活中,影響著我們的日常生活。絕大多數(shù)的網(wǎng)絡(luò)都具有聚類特性,都存在社區(qū)結(jié)構(gòu),如何快速在真實(shí)的網(wǎng)絡(luò)系統(tǒng)中發(fā)現(xiàn)社區(qū)結(jié)構(gòu)已成為社會關(guān)注的熱點(diǎn)之一。針對此種網(wǎng)絡(luò)社區(qū)檢測問題,本文從兩個(gè)方面入手:(1)符號網(wǎng)絡(luò)社區(qū)檢測問題:本文提出一種基于親密度動態(tài)演化的符號網(wǎng)絡(luò)社區(qū)檢測算法。加入了新的相似度計(jì)算函數(shù),并為了使不連接的兩節(jié)點(diǎn)之間有相似度,加入了最短路徑的相似度計(jì)算函數(shù)。構(gòu)造符號網(wǎng)絡(luò)節(jié)點(diǎn)間親密度演化的動力學(xué)模型,從而使同一個(gè)社區(qū)中節(jié)點(diǎn)的親密度隨著時(shí)間的變化更新為1,不同社區(qū)之間節(jié)點(diǎn)的親密度隨著時(shí)間的變化更新為0。在本文所提出網(wǎng)絡(luò)模型的基礎(chǔ)上,整個(gè)網(wǎng)絡(luò)會分為幾個(gè)不同的社區(qū)。為了驗(yàn)證算法的性能,本文針對USC真實(shí)網(wǎng)絡(luò),GGS真實(shí)網(wǎng)絡(luò)以及17個(gè)人工合成網(wǎng)絡(luò)進(jìn)行了仿真,并與已有文獻(xiàn)作了相關(guān)比較,實(shí)驗(yàn)結(jié)果表明算法有一定的優(yōu)勢。(2)動態(tài)網(wǎng)絡(luò)社區(qū)檢測問題:本文在符號網(wǎng)絡(luò)社區(qū)檢測算法的基礎(chǔ)上,加入了時(shí)間的因素。首先對網(wǎng)絡(luò)中上一個(gè)時(shí)刻的相似度和下一個(gè)時(shí)刻的相似度進(jìn)行了加權(quán),其次在Attractor模型上進(jìn)行了改進(jìn),使得親密度高的節(jié)點(diǎn)在一個(gè)社區(qū),最后把網(wǎng)絡(luò)分為不同的社區(qū),并通過真實(shí)網(wǎng)絡(luò)和人工合成網(wǎng)絡(luò)檢測了算法的有效性。為了驗(yàn)證加權(quán)系數(shù)α對該算法的影響,我們對α取了不同的值進(jìn)行比較,發(fā)現(xiàn)α的取值對算法并沒有影響。
[Abstract]:Complex network system exists in our life and affects our daily life. Most networks have clustering characteristics and community structure. How to quickly find community structure in real network system has become one of the hot spots in the society. Aiming at this kind of network community detection problem, this paper starts with two aspects: (1) symbol network community detection problem: this paper proposes a symbol network community detection algorithm based on dynamic evolution of affinity density. A new similarity calculation function is added and the shortest path similarity calculation function is added in order to make the two unconnected nodes have similarity. The dynamic model of the evolution of the affinity between nodes in the symbolic network is constructed, so that the affinity of the nodes in the same community changes with time to 1, and that of the nodes between different communities changes to 0. Based on the proposed network model, the whole network will be divided into several different communities. In order to verify the performance of the algorithm, this paper simulates the USC real network, GGS real network and 17 artificial synthetic networks. The experimental results show that the algorithm has some advantages. (2) the dynamic network community detection problem: this paper adds the time factor to the symbolic network community detection algorithm. Firstly, the similarity between the last moment and the next moment in the network is weighted. Secondly, the Attractor model is improved to make the nodes with high affinity in one community. Finally, the network is divided into different communities. The validity of the algorithm is verified by real network and artificial synthetic network. In order to verify the influence of the weighting coefficient 偽 on the algorithm, we compare the different values of 偽 and find that the value of 偽 has no effect on the algorithm.
【學(xué)位授予單位】：內(nèi)蒙古工業(yè)大學(xué)
【學(xué)位級別】：碩士
【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3條

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本文編號：2380768