The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior

Fudong Wang; Pei Wang; Sunzeng Yao

doi:doi:10.11648/j.hss.20150305.21

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The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior

Fudong Wang, Pei Wang, Sunzeng Yao

Published in Humanities and Social Sciences (Volume 3, Issue 5)

Received: 18 November 2015 Published: 18 November 2015

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Abstract

How to effectively identifying opinion leaders has become a hot research point. It’s essence is to identify the nodes with the strong influence in social network. This paper analyzes the behaviors of users in social networks and proposes the impact evaluation algorithm based on multi-angle user behaviors which is User-Activity Rank algorithm. The algorithm uses the basic idea of PageRank algorithm, considers user’s creativity, interactivity and content quality and designs the uneven distribution mechanism between the users’ UA Rank value, making the computation nodes more accurate. This paper uses Car Home Forum Case for analysis and finds that this algorithm can be more accurate and objective. User-Activity Rank algorithm can help companies improve the accuracy of identifying opinion leaders and promote network marketing with their influence.

Published in	Humanities and Social Sciences (Volume 3, Issue 5)
DOI	10.11648/j.hss.20150305.21
Page(s)	234-239
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Social Network, Opinion Leaders, PageRank, Users’ Behavior

References

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[2]	Tang J, Lou T, Kleinberg J. Inferring social ties across heterogenous networks [C].Proceeding of the 5^th ACM International Conference On Web Search And Data Mining. Seattle,USA,2012:743-752.
[3]	Hon Wai Lam, Chen Wu. Finding Influential eBay Buyers for Viral Marketing－A Conceptual Model of Buyer Rankl[C]. Proceedings of IEEE Conference on Commerce and Enterprise Computing. IEEE, 2009: 778－785.
[4]	Song X,Chi Y,Hino K,Tseng B. Identifying opinion leaders in the blogosphere[C].Proceedings of the 16^th ACM International Conference On Information And Knowledge Management .lisbon, Portugal, 2007: 971-974.
[5]	Ding Z Y,Jia Y,Zhou B,et al.Mining topical influencers based on the multi-relational network in micro-blogging sites [J]. China Communication, 2013,10(1):93-104.
[6]	WENG, Jianshu; LIM, Ee Peng; JIANG, Jing; and He, Qi. Twitterrank: Finding Topic-Sensitive Influential Twitterers [C]. ACM International Conference on Web Search and Data Mining (WSDM 2010).
[7]	Agarwal N, Liu H, Tang l, Yu PS. Identifying the influential bloggers in a community[C]. Proceedings of the 1^st International Conference on Web Search And Data Mining.Palo Alto,USA,2008: 207-21.
[8]	A. Goyal, F. Bonchi, and L. V. Lakshmanan. Learning influence probabilities in social networks [C]. In WSDM’10, 207–217, 2010.
[9]	Goyal A,Bonchi F,Lakshmanan L V S.Discovering leaders from community actions [C]. Proceedings of the 17^th ACM Conference on Information and Knowledge Management. Napa Vally, USA,2008:499-508.
[10]	Michael M. A brief history of generative models for power law and lognormal distributions [J]. Internet Mathematics, 2004, 1(2): 226-251.
[11]	吴信东、李毅、李磊.在线社交网络影响力分析[J].计算机学报.2014.4。
[12]	韩筱璞，汪秉宏，周涛.人类行为动力学研究[J]. 复杂系统与复杂性科学.2010。

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APA Style

Fudong Wang, Pei Wang, Sunzeng Yao. (2015). The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior. Humanities and Social Sciences, 3(5), 234-239. https://doi.org/10.11648/j.hss.20150305.21

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ACS Style

Fudong Wang; Pei Wang; Sunzeng Yao. The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior. Humanit. Soc. Sci. 2015, 3(5), 234-239. doi: 10.11648/j.hss.20150305.21

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AMA Style

Fudong Wang, Pei Wang, Sunzeng Yao. The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior. Humanit Soc Sci. 2015;3(5):234-239. doi: 10.11648/j.hss.20150305.21

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@article{10.11648/j.hss.20150305.21,
  author = {Fudong Wang and Pei Wang and Sunzeng Yao},
  title = {The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior},
  journal = {Humanities and Social Sciences},
  volume = {3},
  number = {5},
  pages = {234-239},
  doi = {10.11648/j.hss.20150305.21},
  url = {https://doi.org/10.11648/j.hss.20150305.21},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20150305.21},
  abstract = {How to effectively identifying opinion leaders has become a hot research point. It’s essence is to identify the nodes with the strong influence in social network. This paper analyzes the behaviors of users in social networks and proposes the impact evaluation algorithm based on multi-angle user behaviors which is User-Activity Rank algorithm. The algorithm uses the basic idea of PageRank algorithm, considers user’s creativity, interactivity and content quality and designs the uneven distribution mechanism between the users’ UA Rank value, making the computation nodes more accurate. This paper uses Car Home Forum Case for analysis and finds that this algorithm can be more accurate and objective. User-Activity Rank algorithm can help companies improve the accuracy of identifying opinion leaders and promote network marketing with their influence.},
 year = {2015}
}

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TY  - JOUR
T1  - The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior
AU  - Fudong Wang
AU  - Pei Wang
AU  - Sunzeng Yao
Y1  - 2015/11/18
PY  - 2015
N1  - https://doi.org/10.11648/j.hss.20150305.21
DO  - 10.11648/j.hss.20150305.21
T2  - Humanities and Social Sciences
JF  - Humanities and Social Sciences
JO  - Humanities and Social Sciences
SP  - 234
EP  - 239
PB  - Science Publishing Group
SN  - 2330-8184
UR  - https://doi.org/10.11648/j.hss.20150305.21
AB  - How to effectively identifying opinion leaders has become a hot research point. It’s essence is to identify the nodes with the strong influence in social network. This paper analyzes the behaviors of users in social networks and proposes the impact evaluation algorithm based on multi-angle user behaviors which is User-Activity Rank algorithm. The algorithm uses the basic idea of PageRank algorithm, considers user’s creativity, interactivity and content quality and designs the uneven distribution mechanism between the users’ UA Rank value, making the computation nodes more accurate. This paper uses Car Home Forum Case for analysis and finds that this algorithm can be more accurate and objective. User-Activity Rank algorithm can help companies improve the accuracy of identifying opinion leaders and promote network marketing with their influence.
VL  - 3
IS  - 5
ER  -

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Author Information

Fudong Wang

Gloria Sun Business School, Donghua University, Shanghai, China
Pei Wang

Gloria Sun Business School, Donghua University, Shanghai, China
Sunzeng Yao

Postgraduate Department, Donghua University, Shanghai, China

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APA Style

Fudong Wang, Pei Wang, Sunzeng Yao. (2015). The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior. Humanities and Social Sciences, 3(5), 234-239. https://doi.org/10.11648/j.hss.20150305.21

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ACS Style

Fudong Wang; Pei Wang; Sunzeng Yao. The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior. Humanit. Soc. Sci. 2015, 3(5), 234-239. doi: 10.11648/j.hss.20150305.21

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AMA Style

Fudong Wang, Pei Wang, Sunzeng Yao. The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior. Humanit Soc Sci. 2015;3(5):234-239. doi: 10.11648/j.hss.20150305.21

Copy | Download

@article{10.11648/j.hss.20150305.21,
  author = {Fudong Wang and Pei Wang and Sunzeng Yao},
  title = {The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior},
  journal = {Humanities and Social Sciences},
  volume = {3},
  number = {5},
  pages = {234-239},
  doi = {10.11648/j.hss.20150305.21},
  url = {https://doi.org/10.11648/j.hss.20150305.21},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20150305.21},
  abstract = {How to effectively identifying opinion leaders has become a hot research point. It’s essence is to identify the nodes with the strong influence in social network. This paper analyzes the behaviors of users in social networks and proposes the impact evaluation algorithm based on multi-angle user behaviors which is User-Activity Rank algorithm. The algorithm uses the basic idea of PageRank algorithm, considers user’s creativity, interactivity and content quality and designs the uneven distribution mechanism between the users’ UA Rank value, making the computation nodes more accurate. This paper uses Car Home Forum Case for analysis and finds that this algorithm can be more accurate and objective. User-Activity Rank algorithm can help companies improve the accuracy of identifying opinion leaders and promote network marketing with their influence.},
 year = {2015}
}

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TY  - JOUR
T1  - The Evaluation of the Social Network’s Nodes Influence Based on Users’ Behavior
AU  - Fudong Wang
AU  - Pei Wang
AU  - Sunzeng Yao
Y1  - 2015/11/18
PY  - 2015
N1  - https://doi.org/10.11648/j.hss.20150305.21
DO  - 10.11648/j.hss.20150305.21
T2  - Humanities and Social Sciences
JF  - Humanities and Social Sciences
JO  - Humanities and Social Sciences
SP  - 234
EP  - 239
PB  - Science Publishing Group
SN  - 2330-8184
UR  - https://doi.org/10.11648/j.hss.20150305.21
AB  - How to effectively identifying opinion leaders has become a hot research point. It’s essence is to identify the nodes with the strong influence in social network. This paper analyzes the behaviors of users in social networks and proposes the impact evaluation algorithm based on multi-angle user behaviors which is User-Activity Rank algorithm. The algorithm uses the basic idea of PageRank algorithm, considers user’s creativity, interactivity and content quality and designs the uneven distribution mechanism between the users’ UA Rank value, making the computation nodes more accurate. This paper uses Car Home Forum Case for analysis and finds that this algorithm can be more accurate and objective. User-Activity Rank algorithm can help companies improve the accuracy of identifying opinion leaders and promote network marketing with their influence.
VL  - 3
IS  - 5
ER  -

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