本文選題：機會網(wǎng)絡(luò) + 重疊社區(qū)��； 參考：《新疆大學(xué)》2014年碩士論文

【摘要】：機會網(wǎng)絡(luò)是一種不需要源節(jié)點和目標(biāo)節(jié)點之間存在完整鏈路，利用節(jié)點移動帶來的相遇機會進(jìn)行通信的新的網(wǎng)絡(luò)模式，對于實現(xiàn)未來普適計算具有重大影響。隨著對實際網(wǎng)絡(luò)的深入研究，研究者們發(fā)現(xiàn)很多實際網(wǎng)絡(luò)中不僅具有社區(qū)結(jié)構(gòu)，而且社區(qū)間存在彼此重疊和相互關(guān)聯(lián)的特性。作為研究網(wǎng)絡(luò)結(jié)構(gòu)的基礎(chǔ)，揭示網(wǎng)絡(luò)中的社區(qū)結(jié)構(gòu)對研究網(wǎng)絡(luò)的功能和分析網(wǎng)絡(luò)的組成結(jié)構(gòu)具有十分重要的意義，重疊社區(qū)檢測成為機會網(wǎng)絡(luò)結(jié)構(gòu)研究的關(guān)鍵問題。 針對機會網(wǎng)絡(luò)中社區(qū)重疊問題，提出一種基于邊權(quán)重局部擴展的重疊社區(qū)檢測方法。算法根據(jù)機會網(wǎng)絡(luò)節(jié)點接觸產(chǎn)生的相遇時間和相遇間隔時間信息，計算節(jié)點間的關(guān)系強度將其作為網(wǎng)絡(luò)中邊的權(quán)重，并使用滑動窗口方法建立機會網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)。然后在得到的網(wǎng)絡(luò)拓?fù)鋱D上，隨機選擇一個節(jié)點標(biāo)記為初始社區(qū)，使用局部擴展方法進(jìn)行擴展，為了使在加權(quán)網(wǎng)絡(luò)中的擴展過程更精確，設(shè)計了一個基于局部適應(yīng)度及內(nèi)密度函數(shù)的優(yōu)化目標(biāo)函數(shù)用來控制社區(qū)擴張，擴展開始時先計算社區(qū)的鄰居節(jié)點對其的歸屬度值，將最大歸屬度值的節(jié)點作為待擴展節(jié)點，再計算目標(biāo)函數(shù)值增加與否，如果增加則把該節(jié)點并入初始社區(qū)，，繼續(xù)向鄰居節(jié)點擴展，否則確定該擴展區(qū)域為一個社區(qū)，然后繼續(xù)選擇下一個未分配社區(qū)的節(jié)點進(jìn)行擴展，直到所有節(jié)點分配了社區(qū)。針對局部擴展方法中存在的初始節(jié)點選擇隨機、重復(fù)計算的不足，給出一種適合加權(quán)重網(wǎng)絡(luò)中的利用節(jié)點聚集系數(shù)對初始節(jié)點進(jìn)行選擇的局部擴展優(yōu)化策略。 為驗證算法性能，本文使用ONE模擬器進(jìn)行實驗仿真，并在該平臺上實現(xiàn)基于社區(qū)的移動模型，并與NBDE算法對產(chǎn)生的仿真數(shù)據(jù)進(jìn)行分析，比較社區(qū)劃分的正確率，實驗表明本文算法能夠較準(zhǔn)確的檢測節(jié)點社區(qū)歸屬，并能夠得到更加精確、穩(wěn)定的重疊社區(qū)結(jié)構(gòu)。
[Abstract]:Opportunistic network is a new network mode which does not need to have a complete link between the source node and the target node, and makes use of the encounter opportunity brought by the node movement to communicate, which has great influence on the realization of future pervasive computing. With the in-depth study of practical networks, researchers have found that many practical networks not only have community structure, but also overlap and correlate with each other among communities. As the basis of studying the network structure, it is very important to reveal the community structure in the network to study the function of the network and to analyze the structure of the network. The overlapping community detection has become the key problem in the research of the opportunity network structure. In order to solve the community overlap problem in opportunity networks, an overlap community detection method based on local expansion of edge weight is proposed. Based on the encounter time and encounter interval information generated by the contact of the nodes in the opportunistic network, the relational strength of the nodes is calculated as the weight of the edges in the network, and the topological structure of the opportunistic network is established by using the sliding window method. Then, on the network topology graph, a node is randomly selected as the initial community, and the local expansion method is used to expand the network, in order to make the expansion process in the weighted network more accurate. An optimization objective function based on local fitness and internal density function is designed to control community expansion. Then calculate whether the value of the objective function is increased or not, if added, merge the node into the initial community, continue to extend to the neighbor node, otherwise determine that the extended area is a community, and then continue to select the node of the next unallocated community for expansion. Until all nodes are assigned to the community. Aiming at the deficiency of random selection and repeated calculation of initial nodes in the local expansion method, a local expansion optimization strategy suitable for the selection of initial nodes using node aggregation coefficients in weighted networks is presented. In order to verify the performance of the algorithm, this paper uses ONE simulator to carry on the experiment simulation, and realizes the community based mobile model on this platform, and analyzes the generated simulation data with the NBDE algorithm, and compares the correct rate of community partition. Experiments show that the proposed algorithm can detect node community ownership accurately and obtain more accurate and stable overlapping community structure.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.02

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相關(guān)期刊論文 前2條

1 王朕;王新華;隋敬麒;;機會網(wǎng)絡(luò)模擬器ONE及其擴展研究[J];計算機應(yīng)用研究;2012年01期

2 吳大鵬;向小華;王汝言;靳繼偉;;節(jié)點歸屬性動態(tài)估計的機會網(wǎng)絡(luò)社區(qū)檢測策略[J];計算機工程與設(shè)計;2012年10期

本文編號：1832835