本文選題：鏈接權重 + 數(shù)據(jù)場�。� 參考：《內(nèi)蒙古大學》2014年碩士論文

【摘要】：隨著信息技術的發(fā)展,產(chǎn)生了大規(guī)模的網(wǎng)絡數(shù)據(jù),這為進行大規(guī)模的網(wǎng)絡分析研究提供了充足的數(shù)據(jù)。近幾年網(wǎng)絡挖掘的研究迅速崛起,并發(fā)展成為一個很熱門的研究領域。鏈接預測是網(wǎng)絡分析的一個重要部分,是一個具有挑戰(zhàn)性的研究方向。本文圍繞網(wǎng)絡數(shù)據(jù)挖掘領域,針對鏈接預測任務展開了深入的研究。在對現(xiàn)有鏈接預測算法分析的基礎上,重點研究了基于結(jié)構(gòu)相似性的鏈接預測算法。針對現(xiàn)有基于結(jié)構(gòu)相似性的鏈接預測方法忽略了網(wǎng)絡拓撲本身鏈接強度的信息,帶權的拓撲路徑方法中權值較難確定等缺陷,提出基于鏈接重要性和數(shù)據(jù)場的鏈接預測算法。該方法將所有鏈接邊賦予不同的鏈接權重,同時考慮潛在鏈接節(jié)點間的相互影響,對部分沒有鏈接的節(jié)點進行鏈接預估計,最后利用數(shù)據(jù)場勢函數(shù)計算兩節(jié)點間的相似值。實驗結(jié)果表明,該方法整體上提高了預測準確性,且參數(shù)確定簡單,有很高的實用價值。另外針對網(wǎng)絡通常是動態(tài)變化,且網(wǎng)絡規(guī)模通常很大,而在應用中,實時性要求又很高,現(xiàn)有的算法復雜度更新代價又較高,難以達到實時要求的現(xiàn)狀,提出了網(wǎng)絡的特定存儲方式以及增量計算方法,達到低代價更新網(wǎng)絡的目的。
[Abstract]:With the development of information technology, large-scale network data is produced, which provides sufficient data for large-scale network analysis. In recent years, the research of network mining has risen rapidly, and has become a very popular research field. Link prediction is an important part of network analysis and a challenging research direction. This paper focuses on the research of link prediction task in the field of network data mining. Based on the analysis of existing link prediction algorithms, a link prediction algorithm based on structural similarity is studied. The existing link prediction methods based on structural similarity ignore the information of the link strength of the network topology itself and the weight value of the weighted topological path method is difficult to determine. A link prediction algorithm based on link importance and data field is proposed. In this method, all link edges are given different link weights, and the interaction between potential link nodes is taken into account, and some nodes without links are pre-estimated. Finally, the similarity between two nodes is calculated by using the potential function of the data field. The experimental results show that the method improves the accuracy of prediction, and the parameter determination is simple, and has high practical value. In addition, the network is usually dynamic, and the network scale is usually very large, but in the application, the real-time requirement is very high, the cost of updating the existing algorithm complexity is high, it is difficult to meet the real-time requirements. The special storage mode and incremental computing method are proposed to update the network at low cost.
【學位授予單位】：內(nèi)蒙古大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP311.13

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本文編號：1887003