【摘要】：隨著內(nèi)置定位芯片智能手機(jī)的廣泛流行，促使傳統(tǒng)的社交網(wǎng)向基于位置的社交網(wǎng)絡(luò)發(fā)展�；谖恢玫纳缃痪W(wǎng)絡(luò)是位置服務(wù)、移動(dòng)互聯(lián)網(wǎng)、以及傳統(tǒng)的社交網(wǎng)絡(luò)結(jié)合的產(chǎn)物，有更廣泛的應(yīng)用場景。 用戶在使用基于位置的社交網(wǎng)絡(luò)時(shí)，會(huì)產(chǎn)生大量的含有地理位置信息的數(shù)據(jù)，如何利用這些由用戶產(chǎn)生的大量的含有地理位置信息的數(shù)據(jù)，，分析用戶的行為模式、運(yùn)動(dòng)軌跡、以及位置感知的用戶社團(tuán)結(jié)構(gòu)等成為了研究熱點(diǎn)。 本文針對(duì)從新浪微博爬取的用戶數(shù)據(jù)，首先分析了用戶的地理位置特征，然后利用地理位置特征進(jìn)行用戶相似性計(jì)算和重疊社團(tuán)的發(fā)現(xiàn)。提出了一種基于地理位置特征的用戶相似度計(jì)算算法和一種基于地理位置特征的重疊社團(tuán)發(fā)現(xiàn)算法，并設(shè)計(jì)實(shí)現(xiàn)了基于地理位置特征的重疊社團(tuán)發(fā)現(xiàn)的可視化工具。論文的具體工作如下： 研究了在基于位置的社交網(wǎng)中用戶相似度的計(jì)算方法，通過分析用戶的含有地理位置數(shù)據(jù)的特征，提出一種基于地理位置特征的用戶相似度計(jì)算方法，并利用從新浪微博中爬取的數(shù)據(jù)驗(yàn)證了算法的有效性。 研究了基于位置的社交網(wǎng)絡(luò)中重疊社團(tuán)的發(fā)現(xiàn)算法，在分析了社交網(wǎng)絡(luò)中用戶關(guān)系以及用戶的地理位置特征基礎(chǔ)上，改進(jìn)邊聚類算法，設(shè)計(jì)并實(shí)現(xiàn)了基于地理位置特征的重疊社團(tuán)發(fā)現(xiàn)算法。最后通過實(shí)驗(yàn)證明了算法的有效性。 本文設(shè)計(jì)并實(shí)現(xiàn)了基于地理位置特征的重疊社團(tuán)發(fā)現(xiàn)的可視化工具。工具主要分為三層：數(shù)據(jù)層、核心層和視圖層。數(shù)據(jù)層完成對(duì)用戶簽到記錄數(shù)據(jù)的封裝以及預(yù)處理；視圖層主要完成顯示功能，并觸發(fā)相應(yīng)的事件；核心層主要是算法的實(shí)現(xiàn)，包含主題提取模塊、相似度計(jì)算模塊、重疊社團(tuán)發(fā)現(xiàn)模塊，同時(shí)該層并負(fù)責(zé)對(duì)視圖層產(chǎn)生的事件的做出響應(yīng)和處理。
[Abstract]:With the popularity of smart phones with built-in locator chips, the traditional social networks are becoming more and more location-based. Location-based social networks are the combination of location services, mobile Internet, and traditional social networks. When users use location-based social networks, they will produce a large amount of data with geographical location information. How to use these data generated by users to analyze the behavior patterns and motion trajectories of users. And location-aware user community structure has become a research hotspot. Based on the user data crawled from Weibo of Sina, this paper first analyzes the geographical location features of users, and then calculates the similarity of users and the discovery of overlapping communities by using geographical location features. A user similarity calculation algorithm based on geographical location feature and an overlapping community discovery algorithm based on geographical location feature are proposed. The visualization tool of overlapping community discovery based on geographical location feature is designed and implemented. The main work of this paper is as follows: the method of calculating user similarity in location-based social network is studied, and the features of users with geographical location data are analyzed. A user similarity calculation method based on geographical location features is proposed, and the validity of the algorithm is verified by crawling data from Sina Weibo. In this paper, the algorithm of discovering overlapping communities in location-based social networks is studied. Based on the analysis of user relationships and geographical location characteristics of users in social networks, the edge clustering algorithm is improved. An overlapping community discovery algorithm based on geographical location features is designed and implemented. Finally, the effectiveness of the algorithm is proved by experiments. This paper designs and implements a visualization tool of overlapping community discovery based on geographical location features. Tools are divided into three layers: data layer, core layer and view layer. The data layer completes the encapsulation and preprocessing of the user check-in record data, the view layer mainly completes the display function and triggers the corresponding events. The core layer is mainly the implementation of the algorithm, including topic extraction module, similarity calculation module, overlapping community discovery module, and the layer is responsible for the response and processing of the events generated by the view layer.
【學(xué)位授予單位】：北京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.09

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本文編號(hào)：2333477