社交信息傳播時序預(yù)測算法
發(fā)布時間：2018-09-12 13:04
【摘要】：日益流行的社交網(wǎng)絡(luò)為信息傳播預(yù)測研究提供了廣泛的數(shù)據(jù)基礎(chǔ)和應(yīng)用場景。信息傳播預(yù)測研究是指基于已知的信息傳播過程,利用方法對社交信息在未來一段時間內(nèi)的傳播趨勢進行預(yù)測,以預(yù)先了解信息傳播的整個過程。借助信息傳播預(yù)測方法,網(wǎng)絡(luò)公司可以更好地為用戶提供個性化推薦服務(wù)和政府部門采取及時有效的輿論控制和引導(dǎo)。信息傳播預(yù)測研究涉及到大規(guī)模數(shù)據(jù)并行處理,社交網(wǎng)絡(luò)拓撲結(jié)構(gòu)分析和文本內(nèi)容分析等多個領(lǐng)域,吸引了來自大數(shù)據(jù)與云計算,復(fù)雜網(wǎng)絡(luò)和自然語言處理等研究領(lǐng)域的學(xué)者們的關(guān)注。信息傳播預(yù)測是社交網(wǎng)絡(luò)研究的一個重要方向,近期的研究方法分為圖和非圖的方法。大多數(shù)非圖的方法采用傳染病模型和分類模型而很少考慮到社交時間序列的聚類特性。在基于聚類的時序預(yù)測算法CTP中,每個聚類質(zhì)心作為一類傳播模式,因此預(yù)測可以通過分類找出預(yù)測對象的最近鄰傳播模式來實現(xiàn),即CTP把預(yù)測對象的最近鄰聚類質(zhì)心作為其預(yù)測結(jié)果。故CTP的預(yù)測性能依賴于預(yù)測對象與其最近鄰聚類質(zhì)心間的擬合度,擬合度越高,則CTP的預(yù)測性能越好。通過分析縮放距離的物理意義,本文觀察到縮放距離能更好度量時間序列間的相似性。本文認為預(yù)測對象的基于縮放距離的最近鄰聚類質(zhì)心可能更加擬合預(yù)測對象從而獲得更高的預(yù)測性能,而CTP的相關(guān)文獻缺乏對預(yù)測性能受到縮放距離影響的研究。故本文基于CTP和縮放距離提出了基于縮放型聚類的時序預(yù)測算法S-CTP,改進后的S-CTP把預(yù)測對象的縮放后的最近鄰聚類質(zhì)心作為預(yù)測結(jié)果以提高其與預(yù)測對象的擬合度進而提高預(yù)測性能。twitter和phrase數(shù)據(jù)集上的實驗結(jié)果表明,S-CTP提高了 CTP的泛化性能。在CTP中,預(yù)測對象的一部分最近鄰聚類成員與預(yù)測對象的相似度較高而另一部分與預(yù)測對象的相似度較低,這導(dǎo)致CTP獲得了較低的預(yù)測性能。針對CTP的預(yù)測性能較低的問題,本文基于CTP和時間序列分段特性提出了基于分段聚類的時序預(yù)測算法D-CTP。為選取與預(yù)測對象最相似的聚類成員,改進后的D-CTP始終把預(yù)測對象作為聚類質(zhì)心并在預(yù)測對象的已知長度時序段進行聚類然后在已知長度和預(yù)測長度時序段精煉聚類質(zhì)心。同S-CTP的提出類似,本文基于D-CTP和縮放距離提出了基于縮放型分段聚類的時序預(yù)測算法。twitter和phrase數(shù)據(jù)集上的實驗結(jié)果表明同時考慮縮放距離和分段聚類的時序預(yù)測算法在S-CTP的基礎(chǔ)上進一步提高了 CTP的泛化性能。
[Abstract]:The increasingly popular social networks provide a wide range of data bases and application scenarios for the prediction of information dissemination. The research of information dissemination prediction is based on the known information dissemination process, using methods to predict the trend of social information in the future, in order to understand the whole process of information dissemination in advance. With the help of information dissemination and prediction method, network companies can better provide personalized recommendation services for users and government departments to take timely and effective public opinion control and guidance. The research of information dissemination prediction involves many fields, such as large-scale data parallel processing, social network topology analysis and text content analysis, which attracts big data and cloud computing. The attention of scholars in the fields of complex networks and natural language processing. Information dissemination prediction is an important research direction in social networks. Recent research methods can be divided into graph and non-graph methods. Most non-graph methods use infectious disease model and classification model, and seldom consider the clustering characteristics of social time series. In the clustering based time series prediction algorithm (CTP), each cluster centroid is regarded as a kind of propagation pattern, so the prediction can be realized by classifying the nearest neighbor propagation pattern of the prediction object. That is, CTP takes the nearest neighbor clustering centroid of the predicted object as its prediction result. Therefore, the prediction performance of CTP depends on the fit between the prediction object and its nearest clustering centroid. The higher the fitting degree is, the better the prediction performance of CTP is. By analyzing the physical meaning of the scaling distance, it is observed that the scaling distance can better measure the similarity between time series. This paper holds that the nearest neighbor centroid based on the scaling distance of the predicted object may be more suitable for the prediction object to obtain higher prediction performance. However, there is a lack of research on the effect of scaling distance on the prediction of CTP. Therefore, based on CTP and zoom distance, this paper proposes a scalable clustering based time series prediction algorithm S-CTP. The improved S-CTP takes the nearest neighbor clustering centroid of the predicted object as the prediction result to improve its fitting degree with the predicted object. The experimental results on the prediction performance. Twitter and phrase datasets show that S-CTP improves the generalization performance of CTP. In CTP, the similarity between some nearest neighbor clustering members and predictive objects is higher, and the other part is lower, which leads to lower prediction performance of CTP. In order to solve the problem of low prediction performance of CTP, a time series prediction algorithm D-CTP based on piecewise clustering is proposed based on the characteristics of CTP and time series segmentation. In order to select the cluster members most similar to the prediction object, the improved D-CTP always takes the prediction object as the cluster centroid and then refines the cluster centroid in the known length time series of the predicted object and the predicted length time series. Similar to S-CTP 's proposal, In this paper, based on D-CTP and zoom distance, a series prediction algorithm based on scalable piecewise clustering. Twitter and phrase data sets are proposed. The experimental results show that the time series prediction algorithm based on S-CTP is based on both zooming distance and segment clustering. The generalization performance of CTP is improved in one step.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09
本文編號：2239087
[Abstract]:The increasingly popular social networks provide a wide range of data bases and application scenarios for the prediction of information dissemination. The research of information dissemination prediction is based on the known information dissemination process, using methods to predict the trend of social information in the future, in order to understand the whole process of information dissemination in advance. With the help of information dissemination and prediction method, network companies can better provide personalized recommendation services for users and government departments to take timely and effective public opinion control and guidance. The research of information dissemination prediction involves many fields, such as large-scale data parallel processing, social network topology analysis and text content analysis, which attracts big data and cloud computing. The attention of scholars in the fields of complex networks and natural language processing. Information dissemination prediction is an important research direction in social networks. Recent research methods can be divided into graph and non-graph methods. Most non-graph methods use infectious disease model and classification model, and seldom consider the clustering characteristics of social time series. In the clustering based time series prediction algorithm (CTP), each cluster centroid is regarded as a kind of propagation pattern, so the prediction can be realized by classifying the nearest neighbor propagation pattern of the prediction object. That is, CTP takes the nearest neighbor clustering centroid of the predicted object as its prediction result. Therefore, the prediction performance of CTP depends on the fit between the prediction object and its nearest clustering centroid. The higher the fitting degree is, the better the prediction performance of CTP is. By analyzing the physical meaning of the scaling distance, it is observed that the scaling distance can better measure the similarity between time series. This paper holds that the nearest neighbor centroid based on the scaling distance of the predicted object may be more suitable for the prediction object to obtain higher prediction performance. However, there is a lack of research on the effect of scaling distance on the prediction of CTP. Therefore, based on CTP and zoom distance, this paper proposes a scalable clustering based time series prediction algorithm S-CTP. The improved S-CTP takes the nearest neighbor clustering centroid of the predicted object as the prediction result to improve its fitting degree with the predicted object. The experimental results on the prediction performance. Twitter and phrase datasets show that S-CTP improves the generalization performance of CTP. In CTP, the similarity between some nearest neighbor clustering members and predictive objects is higher, and the other part is lower, which leads to lower prediction performance of CTP. In order to solve the problem of low prediction performance of CTP, a time series prediction algorithm D-CTP based on piecewise clustering is proposed based on the characteristics of CTP and time series segmentation. In order to select the cluster members most similar to the prediction object, the improved D-CTP always takes the prediction object as the cluster centroid and then refines the cluster centroid in the known length time series of the predicted object and the predicted length time series. Similar to S-CTP 's proposal, In this paper, based on D-CTP and zoom distance, a series prediction algorithm based on scalable piecewise clustering. Twitter and phrase data sets are proposed. The experimental results show that the time series prediction algorithm based on S-CTP is based on both zooming distance and segment clustering. The generalization performance of CTP is improved in one step.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.09
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