本文選題：鏈接預(yù)測(cè) + LBSN��； 參考：《北京交通大學(xué)》2015年碩士論文

【摘要】：基于位置的社交網(wǎng)絡(luò)(location-based social network,LBSN)提供了用戶的在線網(wǎng)絡(luò)關(guān)系和簽到(check-in)的空間時(shí)間等多重信息,連接了虛擬網(wǎng)絡(luò)和現(xiàn)實(shí)生活,不僅豐富了人們的網(wǎng)絡(luò)生活,也為數(shù)據(jù)挖掘和移動(dòng)互聯(lián)網(wǎng)等領(lǐng)域提供了新的研究方向。LBSN的好友關(guān)系預(yù)測(cè)作為其中的一方面,通�？疾炀W(wǎng)絡(luò)結(jié)構(gòu)和空間位置維度的信息,預(yù)測(cè)特征分析較單一。此外,LBSN具有網(wǎng)絡(luò)結(jié)構(gòu)稀疏、預(yù)測(cè)空間大等特點(diǎn),也對(duì)系統(tǒng)預(yù)測(cè)性能提出了挑戰(zhàn)。本文針對(duì)以上問題,在傳統(tǒng)的基于用戶相似性的鏈接預(yù)測(cè)方法基礎(chǔ)上,提出新的LBSN鏈接預(yù)測(cè)系統(tǒng)框架,Brightkite和Gowalla網(wǎng)絡(luò)數(shù)據(jù)仿真結(jié)果表明該系統(tǒng)具有良好的預(yù)測(cè)效果。本文的主要工作和貢獻(xiàn)包括： 1.采用典型的基于位置的社交網(wǎng)絡(luò)Brightkite和Gowalla數(shù)據(jù)集,挖掘LBSN網(wǎng)絡(luò)結(jié)構(gòu)和用戶簽到行為特征。分析表明用戶簽到次數(shù)、地點(diǎn)數(shù)以及簽到地點(diǎn)的訪問人數(shù)、訪問量均呈長(zhǎng)尾分布,同時(shí)發(fā)現(xiàn)部分相對(duì)孤立的用戶和地點(diǎn),需要對(duì)數(shù)據(jù)進(jìn)一步處理。 2.去除地點(diǎn)關(guān)系網(wǎng)絡(luò)(the Co-location Network)中的孤立點(diǎn),解決“僵尸”用戶問題,避免采用朋友-地點(diǎn)關(guān)系(the Co-located Friends Network)網(wǎng)絡(luò)造成保留的用戶過少。 3.采用Louvain算法對(duì)網(wǎng)絡(luò)進(jìn)行社區(qū)劃分,在減小預(yù)測(cè)空間和計(jì)算時(shí)間的同時(shí)提高鏈接預(yù)測(cè)平均準(zhǔn)確率。 4.從網(wǎng)絡(luò)結(jié)構(gòu)和用戶簽到行為多方面挖掘用戶相似性,提出兩類基于簽到時(shí)間和簽到頻率的新的LBSN鏈接預(yù)測(cè)測(cè)度,統(tǒng)計(jì)分析各指標(biāo)與用戶間建立鏈接的平均概率相關(guān)關(guān)系。 5.建立基于相似性的LBSN鏈接預(yù)測(cè)系統(tǒng)框架,進(jìn)行非監(jiān)督和監(jiān)督式鏈接預(yù)測(cè)仿真,分析各指標(biāo)預(yù)測(cè)性能。實(shí)驗(yàn)結(jié)果表明,相比傳統(tǒng)的基于網(wǎng)絡(luò)結(jié)構(gòu)和簽到地點(diǎn)預(yù)測(cè)特征,系統(tǒng)加入基于簽到時(shí)間和簽到頻率的預(yù)測(cè)特征后,整體預(yù)測(cè)效果明顯改善,預(yù)測(cè)準(zhǔn)確率和F1值最高分別提升15.5%和7.4%。
[Abstract]:Location-based social network-based LBSNs provide users with multiple information such as online network relationships and check-in space and time, connecting virtual networks and real life, not only enriching people's network life. It also provides a new research direction for data mining and mobile Internet. LBSN's friend relationship prediction is one of them. As one of them, the information of network structure and spatial location dimension is usually investigated, and the prediction feature analysis is relatively simple. In addition, LBSN has the characteristics of sparse network structure and large prediction space, which also challenges the predictability of the system. Aiming at the above problems, based on the traditional link prediction method based on user similarity, a new LBSN link prediction system framework, Brightkite and Gowalla network data simulation results show that the system has a good prediction effect. The main work and contributions of this paper include: 1. The typical location-based social network Brightkite and Gowalla datasets are used to mine LBSN network structure and user check-in behavior. The analysis shows that the number of users' check-in, the number of site points, the number of visitors and the number of visitors are all long tail distribution. At the same time, it is found that some isolated users and locations need to be processed further. 2. Remove outliers from the Co-location Network, solve the "zombie" user problem, and avoid using the Co-located Friends Network network to create too few reserved users. 3. The Louvain algorithm is used to divide the community of the network, which reduces the prediction space and computation time, and improves the average accuracy of link prediction. 4. In this paper, two kinds of new LBSN link prediction measures based on check-in time and check-in frequency are proposed to mine user similarity from network structure and user check-in behavior, and the average probability correlation between each index and user is analyzed statistically. 5. The LBSN link prediction system framework based on similarity is established, and the unsupervised and supervised link prediction simulation is carried out, and the performance of each index is analyzed. The experimental results show that, compared with the traditional prediction features based on network structure and check-in location, the overall prediction effect is obviously improved by adding the prediction features based on check-in time and check-in frequency. The highest predictive accuracy and F1 value were increased by 15.5% and 7.4%, respectively.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP393.09

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本文編號(hào)：1829090