社交信息傳播時序預測算法
發(fā)布時間：2018-09-12 13:04
【摘要】：日益流行的社交網絡為信息傳播預測研究提供了廣泛的數據基礎和應用場景。信息傳播預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的傳播趨勢進行預測,以預先了解信息傳播的整個過程。借助信息傳播預測方法,網絡公司可以更好地為用戶提供個性化推薦服務和政府部門采取及時有效的輿論控制和引導。信息傳播預測研究涉及到大規(guī)模數據并行處理,社交網絡拓撲結構分析和文本內容分析等多個領域,吸引了來自大數據與云計算,復雜網絡和自然語言處理等研究領域的學者們的關注。信息傳播預測是社交網絡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測算法CTP中,每個聚類質心作為一類傳播模式,因此預測可以通過分類找出預測對象的最近鄰傳播模式來實現,即CTP把預測對象的最近鄰聚類質心作為其預測結果。故CTP的預測性能依賴于預測對象與其最近鄰聚類質心間的擬合度,擬合度越高,則CTP的預測性能越好。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。本文認為預測對象的基于縮放距離的最近鄰聚類質心可能更加擬合預測對象從而獲得更高的預測性能,而CTP的相關文獻缺乏對預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測算法S-CTP,改進后的S-CTP把預測對象的縮放后的最近鄰聚類質心作為預測結果以提高其與預測對象的擬合度進而提高預測性能。twitter和phrase數據集上的實驗結果表明,S-CTP提高了 CTP的泛化性能。在CTP中,預測對象的一部分最近鄰聚類成員與預測對象的相似度較高而另一部分與預測對象的相似度較低,這導致CTP獲得了較低的預測性能。針對CTP的預測性能較低的問題,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測算法D-CTP。為選取與預測對象最相似的聚類成員,改進后的D-CTP始終把預測對象作為聚類質心并在預測對象的已知長度時序段進行聚類然后在已知長度和預測長度時序段精煉聚類質心。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測算法。twitter和phrase數據集上的實驗結果表明同時考慮縮放距離和分段聚類的時序預測算法在S-CTP的基礎上進一步提高了 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.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：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.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.09
【參考文獻】
相關期刊論文 前10條
1 游新年;劉群;;基于傳染病模型的微博信息傳播預測研究[J];計算機應用與軟件;2016年05期
2 李洋;陳毅恒;劉挺;;微博信息傳播預測研究綜述[J];軟件學報;2016年02期
3 周雪峰;徐恪;張藍珊;張賽;;社交網絡的傳播測量與時間序列聚類分析[J];小型微型計算機系統(tǒng);2015年07期
4 孔慶超;毛文吉;;基于動態(tài)演化的討論帖流行度預測[J];軟件學報;2014年12期
5 曹玖新;吳江林;石偉;劉波;鄭嘯;羅軍舟;;新浪微博網信息傳播分析與預測[J];計算機學報;2014年04期
6 毛佳昕;劉奕群;張敏;馬少平;;基于用戶行為的微博用戶社會影響力分析[J];計算機學報;2014年04期
7 王昊;李義萍;馮卓楠;馮鈴;;流行病模型在微博轉發(fā)預測中的應用(英文)[J];中國通信;2013年03期
8 易成岐;鮑媛媛;薛一波;姜京池;;新浪微博的大規(guī)模信息傳播規(guī)律研究[J];計算機科學與探索;2013年06期
9 韓忠明;陳妮;樂嘉錦;段大高;孫踐知;;面向熱點話題時間序列的有效聚類算法研究[J];計算機學報;2012年11期
10 張賽;徐恪;李海濤;;微博類社交網絡中信息傳播的測量與分析[J];西安交通大學學報;2013年02期
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