本文選題：在線社交網絡 + 采樣方法�。� 參考：《北京化工大學》2016年碩士論文

【摘要】：在線社交網絡(Online Social Network, OSN)的興起給網絡帶來了新的革命,同時它自身的很多特性也對現(xiàn)實社會產生了廣泛而深入的影響。近些年來已吸引了很多研究學者對在線社交網絡進行分析和研究。由于在線社交網絡屬于大規(guī)模網絡,其自身特性和行為模式較為復雜,無法準確的獲得真實網絡的全部數(shù)據(jù),所以大部分研究都是基于真實網絡的樣本網絡進行的。對于在線社交網絡的研究,樣本網絡質量對研究結果是極為重要的。因此,通過研究網絡的采樣方法獲得一個能夠反映真實網絡某一方面或某些方面特征的網絡樣本是在線社交網絡研究的前提保障。通過大量的研究,學者們已經提出了多種對于網絡的采樣方法,但是需要一個無偏均勻的樣本集來對這些采樣方法和結果的優(yōu)劣進行評價。而UNI方法采樣獲得的樣本網絡恰好符合要求,它以拒絕-接受采樣為依據(jù)進行無偏均勻的采樣。但該方法也有局限性,僅適用于采集用戶ID系統(tǒng)為32位整數(shù)的網絡,現(xiàn)在大多數(shù)在線社交網絡的用戶ID系統(tǒng)都已經升級為64位整數(shù)系統(tǒng),這就使得表現(xiàn)良好的UNI方法對64位整數(shù)系統(tǒng)的采樣命中率幾乎為零,導致該方法無法繼續(xù)使用。本文采用統(tǒng)計學方法對在線社交網絡用戶64位ID系統(tǒng)的分布情況進行了詳細分析,其結果表明,在線社交網絡用戶ID的分布呈非均勻非隨機分布。根據(jù)此分析結果并結合自適應的思想對UNI方法進行了改進,設計實現(xiàn)一種適用于64位整數(shù)用戶ID系統(tǒng)的高效無偏均勻的自適應采樣方法,稱為“自適應UNI64方法”。最后在新浪微博數(shù)據(jù)集上對該方法的采樣效果進行了實驗驗證,實驗結果表明,自適應UNI64方法能在64位整數(shù)ID系統(tǒng)空間進行采樣,且采樣命中率和采樣效率較UNI方法有很大提高,得到的樣本網絡有效ID的分布符合實際。
[Abstract]:The rise of online Social Network, OSN) has brought a new revolution to the network, and many of its own characteristics have had a wide and deep impact on the real society. In recent years, many researchers have been attracted to analyze and study online social networks. Because the online social network belongs to the large-scale network, its own characteristic and the behavior pattern is more complex, cannot accurately obtain the real network all data, so most of the research is based on the real network sample network. For the research of online social network, the quality of sample network is very important to the research results. Therefore, it is the premise of online social network research to obtain a network sample which can reflect the characteristics of some aspect or some aspect of the real network by studying the sampling method of the network. Through a lot of research, scholars have proposed a variety of sampling methods for the network, but an unbiased uniform sample set is needed to evaluate the merits and demerits of these sampling methods and results. The sample network obtained by UNI method meets the requirements, and it is unbiased and uniform sampling based on rejection-accept sampling. However, this method has its limitations. It is only suitable for the network where the user ID system is a 32-bit integer. Nowadays, most online social network user ID systems have been upgraded to 64-bit integer systems. This makes the good performance of the UNI method to 64-bit integer system sampling hit rate is almost zero, resulting in the method can not continue to use. In this paper, the distribution of 64 bit ID system for online social network users is analyzed in detail by statistical method. The results show that the distribution of online social network user ID is non-uniform and non-random. According to the analysis results and the adaptive idea, the UNI method is improved, and an efficient and unbiased adaptive sampling method for 64-bit integer user ID system is designed and implemented, which is called "adaptive UNI64 method". Finally, the sampling effect of this method is verified on Sina Weibo dataset. The experimental results show that the adaptive UNI64 method can be sampled in 64-bit integer ID system space. The sample hit rate and sampling efficiency are much higher than that of the UNI method, and the distribution of the effective ID of the sample network is in line with the actual situation.
【學位授予單位】：北京化工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP393.09

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本文編號：1973557