Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (10): 72-80    DOI: 10.11925/infotech.1003-3513.2015.10.10
Current Issue | Archive | Adv Search |
Research on Follow Influence of Triadic Structure in Social Network——Take Student Relation Network as an Example
Wu Jiang, Zhang Jinfan
School of Information Management, Wuhan University, Wuhan 430072, China
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] Study on the effects of different triadic structures on follow influence in relation formation. [Methods] This paper uses questionnaires on 221 students at different time to get the dynamic evolution process of this network, and then analyzes the effects of different triadic structures on relation formation. [Results] The results show that triadic structures with reciprocity, transitivity and revesed relationship are more likely to form a new relation. [Limitations] This paper is unable to completely control the influences besides relation network. [Conclusions] The pattern of online and offline relation formation is the same, which is valuable for bussiness.

Received: 14 April 2015      Published: 06 April 2016
:  TP393  

Cite this article:

Wu Jiang, Zhang Jinfan. Research on Follow Influence of Triadic Structure in Social Network——Take Student Relation Network as an Example. New Technology of Library and Information Service, 2015, 31(10): 72-80.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2015.10.10     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2015/V31/I10/72

[1] Wasserman S, Faust K. Social Network Analysis: Methods and Applications [M]. Cambridge University Press, 1994: 25.
[2] Milo R, Shen-Orr S, Itzkovitz S, et al. Network Motifs: Simple Building Blocks of Complex Networks [J]. Science, 2002, 298(5594): 824-827.
[3] Donald H, Wohl R R. Mass Communication and Parasocial Interaction [J]. Psychiatry, 1956, 19(3): 215-229.
[4] Romero D M, Kleinberg J. The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter [C]. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 2010: 138-145.
[5] Tang J, Sun J, Wang C, et al. Social Influence Analysis in Large-scale Networks [C]. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009: 807-816.
[6] Tang J, Wu S, Sun J, et al. Cross-domain Collaboration Recommendation [C]. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2012: 1285-1293.
[7] Tan C, Lee L, Tang J, et al. User-level Sentiment Analysis Incorporating Social Networks [C]. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2011: 1397-1405.
[8] Sun J, Tang J. Models and Algorithms for Social Influence Analysis [C]. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 2013: 775-776.
[9] Heider F. The Psychology of Interpersonal Relations [M]. Psychology Press, 1982.
[10] Klimek P, Thurner S. Triadic Closure Dynamics Drives Scaling Laws in Social Multiplex Networks [J]. New Journal of Physics, 2013, 15(6): Article No.063008.
[11] Li M, Zou H, Guan S, et al. A Coevolving Model Based on Preferential Triadic Closure for Social Media Networks [J]. Scientific Reports, 2013, 3: Article No. 2512.
[12] Lou T, Tang J, Hopcroft J, et al. Learning to Predict Reciprocity and Triadic Closure in Social Networks [J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2013, 7(2): Article No. 5.
[13] Mollenhorst G, Völker B, Flap H. Shared Contexts and Triadic Closure in Core Discussion Networks [J]. Social Networks, 2011, 33(4): 292-302.
[14] Kossinets G, Watts D J. Empirical Analysis of an Evolving Social Network [J]. Science, 2006, 311(5757): 88-90.
[15] Hopcroft J, Lou T, Tang J. Who will Follow You Back?: Reciprocal Relationship Prediction[C]. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, 2011: 1137-1146.
[16] Seshadhri C, Pinar A, Durak N, et al. Directed Closure Measures for Networks with Reciprocity [OL]. arXiv Preprint, 2013. arXiv: 1302.6220.
[17] Snijders T A B, Pattison P E, Robins G L, et al. New Specifications for Exponential Random Graph Models [J]. Sociological Methodology, 2006, 36(1): 99-153.
[18] Robins G, Snijders T, Wang P, et al. Recent Developments in Exponential Random Graph (p*) Models for Social Networks [J]. Social Networks, 2007, 29(2): 192-215.
[19] Kwak H, Lee C, Park H, et al. What is Twitter, a Social Network or a News Media? [C]. In: Proceedings of the 19th International Conference on World Wide Web. ACM, 2010: 591-600.
[20] Gouldner A W. The Norm of Reciprocity: A Preliminary Statement [J]. Journal of Social and Personal Relationships, 1960, 25(2): 161-178.
[21] Watts D J, Strogatz S H. Collective Dynamics of ‘Small-World' Networks [J]. Nature, 1998, 393(6684): 440-442.
[22] McPherson M, Smith-Lovin L, Cook J M. Birds of a Feather: Homophily in Social Networks [J]. Annual Review of Sociology, 2001, 27: 415-444.
[23] Huang H, Tang J, Wu S, et al. Mining Triadic Closure Patterns in Social Networks[C]. In: Proceedings of the 23rd International Conference on World Wide. International World Wide Web Conferences Steering Committee, 2014: 499-504.
[24] Easley D, Kleinberg J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World [M]. Cambridge University Press, 2010.Burt R S. Structural Holes: The Social Structure of Competition [M]. Harvard University Press, 2009.

[1] Chen Jie,Ma Jing,Li Xiaofeng. Short-Text Classification Method with Text Features from Pre-trained Models[J]. 数据分析与知识发现, 2021, 5(9): 21-30.
[2] Li Wenna,Zhang Zhixiong. Research on Knowledge Base Error Detection Method Based on Confidence Learning[J]. 数据分析与知识发现, 2021, 5(9): 1-9.
[3] Sun Yu, Qiu Jiangnan. Research on Influence of Opinion Leaders Based on Network Analysis and Text Mining [J]. 数据分析与知识发现, 0, (): 1-.
[4] Wang Qinjie, Qin Chunxiu, Ma Xubu, Liu Huailiang, Xu Cunzhen. Recommending Scientific Literature Based on Author Preference and Heterogeneous Information Network[J]. 数据分析与知识发现, 2021, 5(8): 54-64.
[5] Li Wenna, Zhang Zhixiong. Entity Alignment Method for Different Knowledge Repositories with Joint Semantic Representation[J]. 数据分析与知识发现, 2021, 5(7): 1-9.
[6] Wang Hao, Lin Kerou, Meng Zhen, Li Xinlei. Identifying Multi-Type Entities in Legal Judgments with Text Representation and Feature Generation[J]. 数据分析与知识发现, 2021, 5(7): 10-25.
[7] Yang Hanxun, Zhou Dequn, Ma Jing, Luo Yongcong. Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism[J]. 数据分析与知识发现, 2021, 5(7): 101-110.
[8] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[9] Huang Mingxuan,Jiang Caoqing,Lu Shoudong. Expanding Queries Based on Word Embedding and Expansion Terms[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[10] Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[11] Ruan Xiaoyun,Liao Jianbin,Li Xiang,Yang Yang,Li Daifeng. Interpretable Recommendation of Reinforcement Learning Based on Talent Knowledge Graph Reasoning[J]. 数据分析与知识发现, 2021, 5(6): 36-50.
[12] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[13] Chen Wenjie,Wen Yi,Yang Ning. Fuzzy Overlapping Community Detection Algorithm Based on Node Vector Representation[J]. 数据分析与知识发现, 2021, 5(5): 41-50.
[14] Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[15] Yan Qiang,Zhang Xiaoyan,Zhou Simin. Extracting Keywords Based on Sememe Similarity[J]. 数据分析与知识发现, 2021, 5(4): 80-89.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn