發(fā)布時間：2018-06-27 02:39

  本文選題：機會網絡 + 社區(qū)劃分�。� 參考：《新疆大學》2017年碩士論文

【摘要】：復雜網絡中存在著一個重要的特性,即社區(qū)特性。機會網絡作為復雜網絡的一種特殊形式,也具有相似特征節(jié)點聚集的現象,即也呈現出了社區(qū)結構的特性。由于機會網絡是根據節(jié)點相遇的機會進行通信的,因此機會網絡的拓撲不斷在改變,所以社區(qū)結構也隨著網絡拓撲的改變處于不斷地變化之中。研究這些動態(tài)社區(qū)有利于更好地理解網絡結構以及更好的利用網絡,針對這個問題,本文主要做了以下研究工作:1、從節(jié)點間社會聯系,關系強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)檢測方法(CDMI)。該方法首先根據單個周期內節(jié)點間的相遇歷史信息計算節(jié)點間的社會壓力指標以及關系強度的值,從而確定相應周期內網絡中哪些節(jié)點間有邊相連。然后計算節(jié)點與節(jié)點之間、節(jié)點與社區(qū)間的親密度。最后根據聚集系數得出種子節(jié)點,從種子節(jié)點進行局部擴展從而完成社區(qū)結構檢測。將仿真結果與節(jié)點動態(tài)歸屬性算法(NBDE)比較,驗證了該方法的準確性與可行性,此外,該方法還能夠得到重疊社區(qū)結構。2、節(jié)點歸屬性是機會網絡中社區(qū)研究的一個重要方面,提出一種基于神經網絡的節(jié)點歸屬性判斷方法。通過將節(jié)點間的相遇頻率、相遇持續(xù)時間、相遇次數作為神經網絡的輸入向量,不斷地調整模型的權值和閥值來進行模型的訓練,訓練完成后,把新節(jié)點組成的向量輸入該模型經過網絡計算即可得出獲勝的神經元,獲勝的神經元就代表輸入數據的分類,以此即可判斷新節(jié)點的歸屬性。在人工數據集LFK基準網絡上測試,結果表明,該方法可以有效地判斷新節(jié)點的歸屬性。
[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.
【學位授予單位】：新疆大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：O157.5

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