在社群網路服務中找出不活躍的使用者

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：20

、訪客IP：18.224.0.25

姓名

徐誌良(Chih-Liang Hsu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

在社群網路服務中找出不活躍的使用者
(Discovering Inactive Users in Social Network Service)

相關論文

★ 應用自組織映射圖網路及倒傳遞網路於探勘通信資料庫之潛在用戶	★ 基於社群網路特徵之企業電子郵件分類
★ 行動網路用戶時序行為分析	★ 社群網路中多階層影響力傳播探勘之研究
★ 以點對點技術為基礎之整合性資訊管理及分析系統	★ 在分散式雲端平台上對不同巨量天文應用之資料區域性適用策略研究
★ 應用資料倉儲技術探索點對點網路環境知識之研究	★ 從交易資料庫中以自我推導方式探勘具有多層次FP-tree
★ 建構儲存體容量被動遷徙政策於生命週期管理系統之研究	★ 應用服務探勘於發現複合服務之研究
★ 利用權重字尾樹中頻繁事件序改善入侵偵測系統	★ 有效率的處理在資料倉儲上連續的聚合查詢
★ 入侵偵測系統：使用以函數為基礎的系統呼叫序列	★ 有效率的在資料方體上進行多維度及多層次的關聯規則探勘
★ 在網路學習上的社群關聯及權重之課程建議	★ 利用階層式權重字尾樹找出在天文觀測紀錄中變化相似的序列

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

社群網路服務在近年來變成熱門的服務, 有越來越多類似的服務在網際網路上開始運作。由於社群網路服務種類非常的多, 使得新手使用者不知道該如何去選擇適合自己的服務。許多使用者沒有從中得到樂趣或是根本不知道使用這個服務的好處在哪。舉個兩個例子來說, 第一種是剛加入社群網路服務的使用者, 他基本上不會知道有誰適合做自己的朋友, 更不會知道有哪些社團是自己可能會感興趣的, 當然跟別人的互動就少。第二種是有些使用者在加入了服務一段時間之後, 因為找不到志同道合的朋友或是找不到這個服務的樂趣在哪, 最後放棄了使用這個社群服務。這兩種使用者可以當作社群網路中的弱勢團體, 通常傾向不活躍的使用者, 因為他們跟其他使用者的互動比較少, 進而導致慢慢的放棄這個服務。為了改善這種現象, 我們期許能夠幫助這些使用者去發掘出社群網路服務的吸引力。一般來說, 一個剛踏入某個社群網路服務的使用者通常充滿著無限的潛力, 因為他們正在熱衷的階段, 也最希望能在這個服務中嘗試新鮮的事物。假如這個新的使用者在使用初期受到了好的指引, 他可能從此愛上這個服務。因此這篇論文對分析社群網路的資料, 提出了一個新的思維。為了要幫助首先我們先找出每個使用者之間發文和回文的時間差異的值, 作為時間的距離, 把這些數值去做處理之後, 去產生出使用者之間的距離。有了這些使用者之間的距離之後, 我們可以更近一步的去做以距離為基礎的離群偵測演算法。這個演算法可以利用點與點之間距離, 去尋找出在某個社群中的離群者。而這些社群中的離群者, 就是我們之前提到的不活躍的使用者。我們可以針對這些不活躍的使用者, 去觀察他們在這個社群服務中的喜好, 像是傾向哪種的分享資源或是偏愛哪一個使用者的發文。有了這方面的資訊之後, 對於該如何提升使用者使用社群網路服務的意願, 會有很大的幫助。

摘要(英)

Social network service (SNS) has become the hot service in recent years. More and more similar services are created on the website. The kinds of services are too lots that many users don’t know how to choose, and some users in the SNS don’t find the enjoyment even after they have used the service. For example, the new members don’t know which guys he/she would like to meet or which groups he/she would feel interesting. In order to improve this phenomenon, we decide to help these users to unearth the attraction of the SNS. Another point is that the major revenue of the social network service is the advertisement on the website. Therefore, it’s significant to think over the effective method to increase the watched times of the advertisement for the designers of the services. However, suppose the entire users see the same kinds of advertisement on their browser, it wouldn’t reach the effective propagation. In other words, our goal is to let different users could see the appropriate advertisement which they may interest on the website. Hence, this paper designs a novel method to analyze the data of SNS on website. At first, we find the time distance between the user nodes, and use these values to represent the distance between the users. In the second, in order to help the users to unearth the attraction of the SNS, we must to find out the target users in the SNS on website first, and then we use these data of distance between the users to run the distance-based algorithm of outlier detection. We called the result data after we computing as inactive users, we can analyze the information of the resources to find which resources do the inactive users interesting and the users who sharing the resources to the inactive users in the SNS on website. The result of the analysis can help us to recommend to the users about which members or groups are better to meet and join.

關鍵字(中)

★ 時間的距離
★ 離群偵測
★ 社群網路服務

關鍵字(英)

★ Outlier Detection
★ Social Network Service
★ Time Distance

論文目次

CHINESE ABSTRACT…… ……………………………………………i
ENGLIST ABSTRACT……………………………………………………iii
TABLE OF CONTENT………………………………………………………v
LIST OF FIGURES………………………………………………………vii
LIST OF TABLES………………………………………………………viii
1 INTRODUCTION…………………………………………………1
2 RELATED WORK…………………………………………………5
2.1 SOCIAL NETWORK SERVICE……………………………………5
2.1.1 Facebook………………………………………………………6
2.1.2 Plurk…………………………………………………………7
2.2 SOCIAL NETWORK ANALYSIS………………………………………7
3 PROBLEM DEFINITIONS………………………………………10
3.1 STRUCTURE OF SOCIAL NETWORK SERVICE…………………10
3.1.1 Structural of User Nodes and User Nodes………………10
3.1.2 Structural of User Nodes and Resource Nodes………11
3.1.3 Structural of the Component we Focus On……………12
3.2 TIME DISTANCE BETWEEN USER NODES………………………13
3.3 OUTLIER DETECTION…………………………………………14
4 SYSTEM ARCHITECTURE………………………………………16
4.1 SELECT THE GROUP DATASET…………………………………17
4.2 INTEGRATE DISTANCE BETWEEN USERS………………………19
4.3 CREATE THE RELATED GRAPH…………………………………22
4.4 SHORTEST DISTANCE BETWEEN USERS………………………23
4.5 GENERATING INACTIVE USERS………………………………23
4.6 DISCUSSION OF THE RESULT………………………………25
5 EXPERIMENTAL RESULTS……………………………………28
5.1 REAL-WORLD DATASET OF GROUP IN FACEBOOK………………29
5.1.1 Decide the Range of the Group Data……………………29
5.1.2 Integrate the Relations between the Users……………31
5.1.3 Create the Related Graph…………………………………32
5.1.4 Generating Inactive Users in Social Network…………33
5.1.5 Result Discussion……………………………………………34
5.2 GROUP DATA COMPARED……………………………………………37
6 CONCLUSIONS AND FUTURE WORK……………………………43
REFERENCE………………………………………………………………45

參考文獻

[1] S. Wasserman and K. Faust, “Social Network Analysis: Methods and
Applications”. Cambridge Univ. Press, 1994.
[2] L. FREEMAN, “Centrality in social networks: Concetpual clarification”, Soc. Netw. 1, pp.215-239, 1979.
[3] J. J. XU and H. CHEN, “CrimeNet Explorer: A Framework for Criminal Network Knowledge Discovery”, ACM Transactions on Information Systems, Vol. 23, No.2, pp201-226, April 2005.
[4] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web”, Proc. Seventh Int’l World Wide Web Conf. (WWW ’98), pp. 161-172, 1998.
[5] J. Kleinberg, “Authoritative Sources in a Hyperlinked Environment”,
J. ACM, vol. 46, no. 5, pp. 604-632, 1999.
[6] S. Wasserman and K. Faust,” Social Network Analysis: Methods and
Applications”. Cambridge Univ. Press, 1994.
[7] V. Krebs, “Mapping Networks of Terrorist Cells”, Connections,
vol. 24, no. 3, pp. 43-52, 2001.
[8] J. Qin, J. Xu, D. Hu, M. Sageman, and H. Chen, “Analyzing Terrorist Networks: A Case Study of the Global Salafi Jihad Network”, Intelligence and Security Informatics, P. Kantor et al., ed., pp. 287-304, Springer, 2005
[9] M. Chau and J. Xu, “Mining communities and their relationships in blogs: A study of online hate groups”. Int. J. Human-Computer Studies 65, pp. 57-70, 2007.
[10] S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient Algorithms for Mining Outliers from Large Data Sets”, Proc. ACM SIGMOD ’00, pp.427-438, 2000.
[11] Edwin Knorr and Raymond Ng. “Algorithms for mining distance-based outliers in large datasets”, In Proc. of the VLDB Conference, pages 392-403, New York, USA, September 1998.
[12] S. Lin and H. Chalupsky, “Discovering and Explaining Abnormal Nodes in Semantic Graphs”. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL.20, NO.8, pp. 1039-1052, AUGUST 2008
[13] M. Sparrow, “The Application of Network Analysis to Criminal. Intelligence: An Assessment of the Prospects” Social Networks, vol. 13, pp. 251-274, 1991.
[14] T. Senator and H. Goldberg, “Break Detection Systems”, Handbook of Knowledge Discovery and Data Mining, W. Kloesgen and J. Zytkow, eds., pp. 863-873, Oxford Univ. Press, 2002.
[15] D. Jensen, M. Rattigan, and H. Blau, “Information Awareness: A Prospective Technical Assessment”, Proc. Ninth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’03), pp. 378-387, 2003.
[16] M. Girvan and M. E. J. Newman, “Community structure in social and biological networks”, Proceedings of the National Academy of Sciences (PNAS), 99:78217826, 2002
[17] M. A. Porter, J. P. Onnela, P. J. Mucha, “Communities in Networks”, Notices of the American Mathematical Society, Vol. 56, No.9, 2009
[18] F.Wu, B. A. Huberman, “Finding communities in linear time: a physics approach”, Eur. Phys. J. B 38 2, 331-338, 2004
[19] R. Albert and A. L. Barab’asi, “Statistical mechanics of complex networks”, Rev. Mod. Phys. 74, 47 – 97, 2002
[20] A. L. Barab’asi, H. Jeong, Z. N’eda, E. Ravasz, A. Schubert, T. Vicsek, “Evolution of the social network of scientific collaborations”, Physica A 311 590 614, 2002
[21] S. Milgram, “The small world problem”, Psychology Today, 2(60), 1967.
[22] I. Pool and M. Kochen, “Contacts and influence”, Social Networks, 1:148, 1978.
[23] H. Zhuge, R. Jia and J. Liu, “Semantic Link Network Builder and Intelligent Browser”, Concurrency and Computation: Practice and Experience, 16(14), pp. 1453-1476, 2004
[24] I. Foster, C. Kesselman, and S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations”, International Journal of High Performance Computing Applications, 15(3), pp. 200-222, 2001
[25] I. Foster, C. Kesselman, J. Nick and S. Tuecke, “The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration”, Open Grid Service Infrastructure WG, Global Grid Forum, 2002.
[26] M. H. Zack, “Researching Organizational Systems Using Social Network Analysis”, Proceedings of the 33rd Annual Hawaii International Conference, 2000.
[27] H. Cho, M. Stefanone and G. Gay, “Social Network Analysis of Information Sharing Networks in a CSCL Community”, Proceedings of Computer Support for Collaborative Learning (CSCL), pp. 43-50, 2002
[28] Y. Upadrashta, J. Vassileva and W. Grassmann, “Social Networks in Peer-to-Peer Systems”, Proceedings of the 38th Annual Hawaii International Conference, 2005.
[29] H. Kautz, B. Selman and M. Shah, “Referral Web: Combining Social Networks and Collaborative Filtering”, Communications of the ACM, 40(3), pp. 63-65, 1997
[30] B. Yu and M. P. Singh, “Searching Social Networks”, Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), ACM Press, 2003.
[31] M. E. J. Newman, D. J. Watts and S. H. Strogatz, “Random Graph Models of Social Networks”, Proceedings of the National Academy of Sciences, pp. 2566-2572, 2002
[32] A. Buscarino, L. Fortuna, M. Frasca, A. S. Fiore, “A New Model for Growing Social Networks”, Proceedings of the IEEE computer society, pp. 103-105, 2010

指導教授

蔡孟峰(Meng-Feng Tsai)

審核日期

2010-7-28

推文