本文選題：微博 + 社交網絡　； 參考：《西華大學》2016年碩士論文

【摘要】：隨著互聯網時代的到來,網絡漸漸地融入人們的生活。許多網民通過互聯網進行購物、交友、學習等日�；顒�,它已經成為人們生活中十分重要的一部分。在人們的互聯網生活中,網絡社交平臺,如:新浪微博、騰訊微博、Twitter等,已經成為了眾多網民較為活躍的場所。人們可以在這些社交平臺中結交新朋友,并與其他用戶分享他們感興趣的文字、圖片、視頻等信息,而這些被用戶發(fā)布的信息在一定程度上反映出了用戶的行為習慣和興趣愛好。就目前來說,社交數據具有內容簡短、數量龐大、實時性高等特點,因此從海量社交數據中挖掘出有效的信息是數據挖掘領域的一大挑戰(zhàn)。面對著大量的社交平臺用戶數據,構建用戶的社交圖譜和興趣圖譜是提高社交網絡中社交搜索質量的關鍵。針對與上述問題,為了有效地構建出用戶的社交圖譜和興趣圖譜,本文的主要研究內容包含有以下幾點:1.本文基于鏈路預測(Link Prediction)的思想,通過改進Friend Link(FL)算法,提出了活躍朋友的預測算法(Active Friend Prediction,AFP)。為了適用于微博這類擁有稀疏的用戶屬性信息的在線社交平臺,本文將用戶的在線社交網絡抽象為有向圖(其中節(jié)點代表用戶、邊代表用戶之間存在關系),通過圖的局部鏈路特征來分析用戶之間的相似度。本文提出了節(jié)點活躍系數的概念,即利用各個節(jié)點的出度和入度,通過它們的比值來刻畫節(jié)點的活躍程度,進而從用戶的社交網絡圖中篩選出行為活躍的用戶。同時結合社交網絡圖的節(jié)點之間的鏈路結構相似度來計算出節(jié)點的活躍度評分,從而根據該評分提取出與用戶有潛在關系的活躍間接鄰居,并利用這些節(jié)點構建出用戶的高活躍度局部社交網絡,即用戶的社交圖譜。2.本文提出了用戶關注的隱式和顯式熱點人物提取算法(Focusing Personae Extraction algorithm,FPE)。微博是一種以短文本為信息載體的社交平臺,雖然微博文本包含著用戶關注的人物實體,但是,這些文本中總是充斥著大量的噪聲信息。因此,本文從用戶及其社交圖譜中的用戶所發(fā)表的微博中提取出人物實體,根據目標用戶社交圖譜中用戶的活躍度評分以及包含了相關的人物實體的微博條數,從而計算出用戶對人物實體的關注度,并將具有較高關注度的人物實體作為熱點人物構建出用戶的熱點人物興趣圖譜。此外,該方法還可以用來提取整個局部社交網絡中被關注的熱點人物。最后,本文通過對比實驗的方式,比較了不同的基于鏈路的節(jié)點相似度計算方法與本文改進的算法在精確度、召回率、F值以及時間效率上的差異,并且分別在基于不同的鏈路預測算法所構建出的目標用戶社交圖譜中提取用戶關注的熱點人物。最終實驗證明,本文改進的節(jié)點評分計算方法較其他方法來說有較高的精確度、召回率、F值,此外本文提出的隱式和顯式熱點人物實體提取方法能夠有效地挖掘出用戶所關注的熱點人物,并且其精確度取決于用戶社交圖譜的精確度。
[Abstract]:With the advent of the Internet era, the network has gradually integrated into people's life. Many netizens have been shopping, making friends, learning and other daily activities through the Internet. It has become a very important part of people's life. In people's Internet life, network social platforms, such as Sina micro-blog, Tencent micro-blog, Twitter and so on, have already become There are many more active sites for Internet users. People can make new friends in these social platforms and share with other users the words, pictures and videos that they are interested in, and the information that is published by the user reflects the behavior habits and interests of the users to some extent. It is short, large and real-time, so mining effective information from mass social data is a major challenge in the field of data mining. Facing a large number of social platform user data, building user's social network and interest atlas is the key to improve the quality of social networks. The main contents of this paper are as follows: 1. in this paper, based on the idea of link prediction (Link Prediction), and by improving the Friend Link (FL) algorithm, a prediction algorithm for active friends (Active Friend Prediction, AFP) is proposed. In order to apply to micro-blog, it is sparse. In this paper, the online social platform of user attribute information is used to abstract the user's online social network into a directed graph (the node represents the user and the side represents the relationship between the users). The similarity between the users is analyzed by the local link characteristics of the graph. The concept of the node activity coefficient is proposed in this paper, that is, the output of each node is used. The ratio is used to characterize the activity of the node, and then the active users are screened from the user's social network graph, and the link structure similarity between the nodes of the social network graph is used to calculate the activity score of the node, thus extracting the active indirect relationship with the user. Neighbors, and use these nodes to build a user's high activity local social network, that is, the user's social map.2. proposed the implicit and explicit hot spot extraction algorithm (Focusing Personae Extraction algorithm, FPE). Micro-blog is a social platform with short text as the information carrier, although micro-blog text packet The text contains a lot of noise information, which is always full of the user's attention. Therefore, this article extracts the entity from the micro-blog in the user and its social atlas, according to the user's activity score in the target user's social map and the micro-blog number that contains the related entity. In addition, this method can also be used to extract hot people who are concerned in the whole local social network. Finally, this paper compares the different methods of the experiment to compare the different kinds of hot spots in the whole local social network. The link based method of node similarity calculation is different from the improved algorithm in accuracy, recall, F value and time efficiency, and extracts the hotspots of the user's attention in the target user social map based on the different link prediction algorithms. The final experiment proves that the improved node score is improved. The calculation method has higher accuracy, recall and F value than other methods. In addition, the implicit and explicit hot spot entity extraction methods proposed in this paper can effectively excavate the hot spots of the user's attention, and its accuracy depends on the accuracy of the user's social map.
【學位授予單位】：西華大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP393.092;TP391.1

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