本文選題：機(jī)會(huì)網(wǎng)絡(luò) + 社區(qū)劃分��； 參考：《新疆大學(xué)》2017年碩士論文

【摘要】：復(fù)雜網(wǎng)絡(luò)中存在著一個(gè)重要的特性,即社區(qū)特性。機(jī)會(huì)網(wǎng)絡(luò)作為復(fù)雜網(wǎng)絡(luò)的一種特殊形式,也具有相似特征節(jié)點(diǎn)聚集的現(xiàn)象,即也呈現(xiàn)出了社區(qū)結(jié)構(gòu)的特性。由于機(jī)會(huì)網(wǎng)絡(luò)是根據(jù)節(jié)點(diǎn)相遇的機(jī)會(huì)進(jìn)行通信的,因此機(jī)會(huì)網(wǎng)絡(luò)的拓?fù)洳粩嘣诟淖?所以社區(qū)結(jié)構(gòu)也隨著網(wǎng)絡(luò)拓?fù)涞母淖兲幱诓粩嗟刈兓�。研究這些動(dòng)態(tài)社區(qū)有利于更好地理解網(wǎng)絡(luò)結(jié)構(gòu)以及更好的利用網(wǎng)絡(luò),針對(duì)這個(gè)問(wèn)題,本文主要做了以下研究工作:1、從節(jié)點(diǎn)間社會(huì)聯(lián)系,關(guān)系強(qiáng)度以及親密度的綜合考慮,提出了一種基于親密度的社區(qū)檢測(cè)方法(CDMI)。該方法首先根據(jù)單個(gè)周期內(nèi)節(jié)點(diǎn)間的相遇歷史信息計(jì)算節(jié)點(diǎn)間的社會(huì)壓力指標(biāo)以及關(guān)系強(qiáng)度的值,從而確定相應(yīng)周期內(nèi)網(wǎng)絡(luò)中哪些節(jié)點(diǎn)間有邊相連。然后計(jì)算節(jié)點(diǎn)與節(jié)點(diǎn)之間、節(jié)點(diǎn)與社區(qū)間的親密度。最后根據(jù)聚集系數(shù)得出種子節(jié)點(diǎn),從種子節(jié)點(diǎn)進(jìn)行局部擴(kuò)展從而完成社區(qū)結(jié)構(gòu)檢測(cè)。將仿真結(jié)果與節(jié)點(diǎn)動(dòng)態(tài)歸屬性算法(NBDE)比較,驗(yàn)證了該方法的準(zhǔn)確性與可行性,此外,該方法還能夠得到重疊社區(qū)結(jié)構(gòu)。2、節(jié)點(diǎn)歸屬性是機(jī)會(huì)網(wǎng)絡(luò)中社區(qū)研究的一個(gè)重要方面,提出一種基于神經(jīng)網(wǎng)絡(luò)的節(jié)點(diǎn)歸屬性判斷方法。通過(guò)將節(jié)點(diǎn)間的相遇頻率、相遇持續(xù)時(shí)間、相遇次數(shù)作為神經(jīng)網(wǎng)絡(luò)的輸入向量,不斷地調(diào)整模型的權(quán)值和閥值來(lái)進(jìn)行模型的訓(xùn)練,訓(xùn)練完成后,把新節(jié)點(diǎn)組成的向量輸入該模型經(jīng)過(guò)網(wǎng)絡(luò)計(jì)算即可得出獲勝的神經(jīng)元,獲勝的神經(jīng)元就代表輸入數(shù)據(jù)的分類(lèi),以此即可判斷新節(jié)點(diǎn)的歸屬性。在人工數(shù)據(jù)集LFK基準(zhǔn)網(wǎng)絡(luò)上測(cè)試,結(jié)果表明,該方法可以有效地判斷新節(jié)點(diǎn)的歸屬性。
[Abstract]:There is an important characteristic in complex networks, that is, community characteristics. As a special form of complex network, opportunistic network also has the phenomenon of similar characteristic node aggregation, that is, it also presents the characteristics of community structure. Because the opportunistic network communicates according to the chance that the nodes meet, the topology of the opportunistic network is constantly changing, so the community structure is constantly changing with the change of the network topology. Studying these dynamic communities is conducive to a better understanding of the network structure and better use of the network. In view of this problem, this paper mainly does the following research work: 1, from the social connection between nodes, the relationship strength and the comprehensive consideration of the affinity. A community detection method based on affinity (CDMI) is proposed. The method first calculates the social pressure index and the relationship strength between nodes according to the historical information of the encounter between nodes in a single cycle, and then determines which nodes in the corresponding cycle are connected with each other. Then the affinity between nodes and communities is calculated. Finally, the seed node is obtained according to the aggregation coefficient, and the community structure is detected by the local expansion from the seed node. The simulation results are compared with the node dynamic attribute algorithm (NBDE), and the accuracy and feasibility of the method are verified. In addition, the method can obtain overlapping community structure. The node attribute is an important aspect of community research in the opportunistic network. In this paper, a neural network based method for determining the attribute of nodes is proposed. The frequency, duration and number of encounters are used as input vectors of the neural network, and the weights and thresholds of the model are constantly adjusted to train the model. Input the vector of the new node into the model, the winning neuron can be obtained by network calculation, and the winning neuron can represent the classification of the input data, and then the attribute of the new node can be judged. The test results on the LFK benchmark network show that the proposed method can effectively judge the attribute of the new node.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：O157.5

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