一種基于最大熵原理的社交網(wǎng)絡用戶關系分析模型

肖云鵬; 楊光; 劉宴兵; 吳斌

doi:10.11999/JEIT160605

一種基于最大熵原理的社交網(wǎng)絡用戶關系分析模型

doi: 10.11999/JEIT160605 cstr: 32379.14.JEIT160605

1.
(重慶郵電大學網(wǎng)絡與信息安全技術重慶市工程實驗室重慶 400065) ②(北京郵電大學北京市智能通信軟件與多媒體重點實驗室北京 100876)

基金項目:

國家973計劃項目(2013CB329606), 國家自然科學基金(61272400), 重慶市青年人才項目(cstc2013kjrc-qnrc 40004)，教育部-中國移動研究基金(MCM20130351)，重慶市研究生研究與創(chuàng)新項目(CYS14146)，重慶市教委科學計劃項目(KJ1500425)，重慶郵電大學文峰基金(WF201403)

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出版歷程
- 收稿日期: 2016-06-07
- 修回日期: 2016-11-30
- 刊出日期: 2017-04-19

Social Relationship Analysis Model Based onthe Principle of Maximum Entropy

1.
(Chongqing Engineering Laboratory of Internet and Information Security, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
2.
(The Intelligent Communication Software and Multimedia Key Laboratory of Beijing, Beijing University of Posts and Telecommunications, Beijing 100876, China)

Funds:

The National 973 Program of China (2013CB 329606), The National Natural Science Foundation of China (61272400), Chongqing Youth Innovative Talent Project (cstc2013 kjrc-qnrc40004), Ministry of Education of China and China Mobile Research Fund (MCM20130351), Chongqing Graduate Research and Innovation Project (CYS14146), Science and Technology Research Program of the Chongqing Municipal Education Committee (KJ1500425), WenFeng Foundation of CQUPT (WF201403)

摘要

摘要: 在社交網(wǎng)絡的演化和發(fā)展過程中，用戶之間關系的建立受到多種因素的共同作用。該文通過對社交網(wǎng)絡中用戶屬性以及用戶關系數(shù)據(jù)進行分析，旨在發(fā)現(xiàn)影響用戶關系建立的關鍵因素。首先，針對用戶關系建立的復雜驅動因素，分別從個人興趣、好友關系、社團驅動3個方面提取影響用戶關系建立的因素并定義相應的影響因子函數(shù)。其次，針對多種影響因素難以量化以及權值分配不確定等問題，以最大熵原理為基礎構建用戶關系分析模型，該模型在選擇特征時具有不需要依賴于特征之間的關聯(lián)性等特點，并能夠量化各個因素對用戶關系建立的驅動強度。從而挖掘影響鏈接建立的關鍵因素，分析用戶關系發(fā)展態(tài)勢。實驗表明，該模型不僅能夠量化各因素對鏈接建立的驅動強度，發(fā)現(xiàn)關鍵影響因素，而且可以對用戶關系進行有效預測。
- 社交網(wǎng)絡 /
- 用戶關系 /
- 關系態(tài)勢 /
- 最大熵原理
Abstract: Within the evolution and development of social networks, the establishment of relationships among the users is affected by various factors. By analyzing user behavior data and relationship data in social network, this study tries to detect the key factors that affect the formation of relationship among users. Firstly, considering the complex driving factors for the user relationship establishment, the factors are extracted and the impact factor functions are defined from personal attributes, friendships and community driving. Secondly, in order to quantify driving factors and assign weight, a user relationship analysis model based on the principle of maximum entropy is proposed. The model is, when choosing features, characterized by its independence from?the association among features, and can also quantify the strength of various factors that drive users to establish relationship. Furthermore, the key factors that affect the user relationship can be detected and the development trend of user relationship can be analyzed. Experimental results reveal that the proposal model can not only quantify the strength of each factor that drives relationship establishment, it can also predict the user relationship effectively.
- Social network /
- User relationship /
- Situation analysis /
- Principle of maximum entropy

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參考文獻(21)

WANG P, XU B W, WU Y R, et al. Link prediction in social networks: The state-of-the-art[J]. Science China Information Sciences, 2015, 58(1): 1-38. doi: 10.1007/s11432-014-5237-y.

L L and ZHOU T. Link prediction in complex networks: A survey[J]. Physica A: Statistical Mechanics and Its Applications, 2011, 390(6): 1150-1170. doi: 10.1016/j.physa. 2010.11.027.

ZHU B and XIA Y. Link prediction in weighted networks: A weighted mutual information model[J]. PloS One, 2016, 11(2): e0148265. doi: 10.1371/journal.pone.0148265.

YUAN N J, ZHONG Y, ZHANG F, et al. Who will reply to/retweet this tweet: The dynamics of intimacy from online social interactions[C]. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, California, USA. 2016: 3-12. doi: 10.1145/ 2835776.2835800.

KAHANDA I and JENNIFER N. Using transactional information to predict link strength in online social networks[C]. International AAAI Conference on Weblogs and Social Media, California, USA, 2009: 74-81.

XIANG Rongjing, NEVILLE J, and ROGATI M. Modeling relationship strength in online social networks[C]. Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina, USA, 2010: 981-990. doi: 10.1145/1772690.1772790.

KASHIMA H, KATO T, YAMANISHI Y, et al. Link propagation: A fast semi-supervised learning algorithm for link prediction[C]. Proceedings of the SIAM International Conference on Data Mining, Nevada, USA, 2009, 9: 1099-1110. doi: 10.1137/1.9781611972795.94.

JONES J J, SETTLE J E, BOND R M, et al. Inferring tie strength from online directed behavior[J]. PloS One, 2013: e52168. doi: 10.1371/journal.pone.0052168.

SHAO C, DUAN Y, and WANG B. Attractive density: a new node similarity index of link prediction in complex networks[C]. IEEE International Conference on Information Science and Technology, Changsha, China, 2015: 74-78. doi: 10.1109/icist.2015.7288943.

ADAMIC L A and ADAR E. Friends and neighbors on the web[J]. Social Networks, 2003, 25(3): 211-230. doi: 10.1016/ s0378-8733(03)00009-1.

KATZ L. A new status index derived from sociometric analysis[J]. Psychometrika, 1953, 18(1): 39-43. doi: 10.1007/ bf02289026.

CHEN Y X and CHEN L. Random walks for link prediction in networks with nodes attributes[C]. Network Security and Communication Engineering: Proceedings of the 2014 International Conference on Network Security and Communication Engineering, Hong Kong, 2015: 291. doi: 10.1201/b18660-64.

XIE X, LI Y, ZHANG Z, et al. A joint link prediction method for social network[C]. International Conference of Young Computer Scientists, Engineers and Educators, Harbin, China, 2015: 56-64. doi: 10.1007/978-3-662- 46248-5_8.

LIU W and LU L. Link prediction based on local random walk[J]. Europhysics Letters, 2010, 89(5): 58007-58012. doi: 10.1209/0295-5075/89/58007.

LIU H, HU Z, HADDADI H, et al. Hidden link prediction based on node centrality and weak ties[J]. Europhysics Letters, 2013, 101(1): 18004-18009. doi: 10.1209/0295-5075/ 101/18004.

AHMED C, LKORANY A, and BAHGAT R. A supervised learning approach to link prediction in Twitter[J]. Social Network Analysis and Mining, 2016, 6(1): 1-11. doi: 10.1007/ s13278-016-0333-1.

AHEMD C and ELKORANY A. Enhancing Link prediction in twitter using Semantic user attributes[C]. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, 2015: 1155-1161. doi: 10.1145/2808797.2810056.

BERGER A L, PIETRA V J D, and PIETRA S A D. A maximum entropy approach to natural language processing [J]. Computational Linguistics, 1996, 22(1): 39-71. doi: 10.1007/springerreference_179232.

PAKZAD F and ABHARI A. Characterization of user networks in Facebook[C]. Proceedings of the 2010 Spring Simulation Multiconference. Society for Computer Simulation International, Orlando, FL, USA, 2010: 105. doi: 10.1145/1878537.1878647.

PARK B, LEE K, and KANG N. The impact of influential leaders in the formation and development of social networks[C]. Proceedings of the 6th International Conference on Communities and Technologies ACM, Munich, Germany, 2013: 8-15. doi: 10.1145/2482991.2483004.

TANG X and YANG C C. Ranking user influence in healthcare social media[J]. ACM Transactions on Intelligent Systems and Technology, 2012, 3(4): 565-582. doi: 10.1145/ 2337542.2337558.

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