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  本文選題：復雜網(wǎng)絡 + 社區(qū)挖掘��； 參考：《吉林大學》2012年博士論文

【摘要】：復雜網(wǎng)絡社區(qū)挖掘是近十年最前沿的多學科交叉研究熱點之一，已被廣泛應用于恐怖組織識別、蛋白質(zhì)功能預測、搜索引擎等諸多領域。本文基于蟻群算法、遺傳算法、馬爾科夫動力學方法，對社區(qū)挖掘問題進行研究。 提出基于蟻群算法的社區(qū)挖掘方法RWACO。它結合了馬爾科夫隨機游走模型及集成學習思想，通過“強化社區(qū)內(nèi)連接、弱化社區(qū)間連接”這一進化策略使社區(qū)結構逐漸呈現(xiàn)。實驗表明，RWACO較一些代表性算法具有更高的聚類精度。 提出基于遺傳算法的社區(qū)挖掘方法GALS。它采用了基于圖的編碼策略LAR，以模塊性函數(shù)Q作為目標函數(shù)。針對傳統(tǒng)變異方法之不足，，我們面向LAR編碼給出邊緣基因的概念；推導出模塊性函數(shù)Q之局部單調(diào)性；在上述兩點的基礎上提出了一個快速有效的局部搜索變異算法。在人工網(wǎng)絡及真實網(wǎng)絡上進行測試，并與當前代表性算法進行比較，實驗表明了GALS的有效性。 提出基于馬爾科夫動力學的重疊社區(qū)挖掘算法UEOC。首先將原始網(wǎng)絡與相應的退火網(wǎng)絡融合為一個集成網(wǎng)絡，在集成網(wǎng)絡上給出一個基于約束的馬爾科夫動力學新模型，以逐步呈現(xiàn)網(wǎng)絡中的每個社區(qū)。然后基于局部社區(qū)函數(shù)“導電率”，設計一個有效的截方法，將已呈現(xiàn)出的社區(qū)抽取出來。如果網(wǎng)絡具有重疊結構，被抽取出的社區(qū)則天然呈現(xiàn)重疊現(xiàn)象。實驗表明，UEOC可快速有效的發(fā)現(xiàn)重疊社區(qū)結構。
[Abstract]:The mining of complex online communities is one of the most advanced interdisciplinary research hotspots in the past decade. It has been widely used in terrorist tissue identification, protein function prediction, search engine and many other fields. Based on ant colony algorithm, genetic algorithm and Markov dynamics, community mining problem is studied in this paper. A community mining method based on ant colony algorithm (RWACO) is proposed. It combines Markov random walk model with the idea of integrated learning, and makes the community structure appear gradually through the evolutionary strategy of "strengthen the connection within the community and weaken the connection between the communities". Experiments show that RWACO has higher clustering accuracy than some representative algorithms. A community mining method based on genetic algorithm (GALS-based) is proposed. It adopts the graph-based coding strategy LAR and takes the modular function Q as the objective function. In view of the shortcomings of traditional mutation methods, we give the concept of edge gene for LAR coding, deduce the local monotonicity of modular function Q, and propose a fast and effective local search mutation algorithm based on the above two points. The experiments on artificial network and real network show the effectiveness of GALS. An overlapping community mining algorithm based on Markov dynamics is proposed. Firstly, the original network and the corresponding annealing network are merged into an integrated network, and a new constrained Markov dynamics model is presented in the integrated network to gradually present each community in the network. Then, based on the local community function "conductivity", an effective truncation method is designed to extract the existing community. If the network has overlapping structure, the extracted community is naturally overlapped. Experiments show that UUOC can quickly and effectively find overlapping community structures.
【學位授予單位】：吉林大學
【學位級別】：博士
【學位授予年份】：2012
【分類號】：TP311.13;O157.5

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相關期刊論文 前6條

1 金弟;劉大有;楊博;劉杰;何東曉;田野;;基于局部探測的快速復雜網(wǎng)絡聚類算法[J];電子學報;2011年11期

2 何東曉;周栩;王佐;周春光;王U

本文編號：1873363