Username   Password       Forgot your password?  Forgot your username? 

A Study on the Influence Propagation Model in Topic Attention Networks

Volume 13, Number 5, September 2017 - Paper 15  - pp. 721-730
DOI: 10.23940/ijpe.17.05.p15.721730

Xiao Chena,b,d, Jingfeng Guoa,d,*, Kelun Tiana, Chaozhi Fana, Xiao Panc

aCollege of Information Science and Engineering, YanShan University, Qinhuangdao, 066004, China
bQian’an College, North China University of Science and Technology, Qian’an, 064400, China
cCollege of Economic and Management, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
dThe Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, China

(Submitted on March 26, 2017; Revised on June 16, 2017; Accepted on August 13, 2017)


The social networks with the complex user relations and huge amount of data and hidden information, bring new opportunities and challenges for the study of information diffusion and influence maximization. In recent years, there are more and more researches on the influence maximization of topic preference. However, most of the existing researches only take the topic as an attribute of the users, and the importance of the topic in network structure is not considered. In view of this situation, firstly, this paper constructed a new topic attention network model fusing the social relation and the topic preference. Secondly, based on connected degree of set pair and Markov random walk model, we propose the calculated method of the topic preference for users, and then mining the seed set with influence by the greedy strategy. Thirdly, we propose the calculated method of the activation probability of the user based on the user relation and the topic preference, and propose the influence maximization algorithm TAN_CELF in topic attention networks. Finally, on Dou-ban network dataset, from three metrics ISST, ISRT and ISRNT, compare with algorithm L_GAUP and CELF, the experimental results show that algorithm TAN_CELF that is proposed by this paper has a higher performance on influence scope.


References: 17

    1. N. Barbieri, F. Bonchi, and G. Manco. “Topic-Aware Social Influence Propagation Models” in Proceedings of the 8th International Conference on Data Mining, pp. 81-90, Las Vegas Nevada, USA, July, 2012
    2. A. Goyal, F. Bonchi, and L. V. S. Lakshmanan. “Learning Influence Probabilities in Social Networks” in Proceedings of the 3th International Conference on Web Search and Web Data Mining, WSDM 2010, pp. 241-250, New York, USA, February, 2010
    3. J. Guo, P. Zhang, and C. Zhou. “Personalized Influence Maximization on Social Networks” in Proceedings of the 22th ACM International Conference on Information & Knowledge Management, pp. 199-208, San Francisco, USA, October, 2013
    4. J. F. Guo, and J. G. Lv. “Influence Maximization Based on Information Preference,” Journal of Computer Research and Development, no. 02, pp. 533-541, February, 2015
    5. D. Kempe, J. Kleinberg, and É. Tardos. “Maximizing the Spread of Influence Through A Social Network” in Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137-146, Washington, USA, August, 2003
    6. M. Kimura and K. Saito. “TracTable Models for Information Diffusion in Social Networks” in Proceedings of the 17th Knowledge Discovery in Databases: PKDD 2006,  pp. 259-271, Berlin, Germany, September, 2006
    7. L. Q. Kong, and M. L. Yang. “Improvement of Clustering Algorithm FEC for Signed Networks,” Journal of Computer Applications, vol. 31, no. 5, pp. 1395-1399, May, 2011
    8. J. Leskovec, A. Krause, and C. Guestrin. “Cost-effective Outbreak Detection in Networks” in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420-429, San Jose, California, USA, August, 2007
    9. L. Liu, J. Tang, and J. Han. “Mining Topic-Level Influence in Heterogeneous Networks” in Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, pp. 199-208, Toronto, Ontario, Canada, October, 2010
    10. J. G. Lv and J. F. Guo. “Mining communities in social network based on information diffusion,” Ieej Transactions on Electrical & Electronic Engineering, vol. 11, no. 5, pp. 604-617, July, 2016
    11. M. Richardson and P. Domingos. “Mining Knowledge-Sharing Sites for Viral Marketing” in Proceedings of the 8th Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61-70, Edmonton, Canada, August, 2002
    12. K. Saito, M. Kimura and K. Ohara. “Behavioral Analyses of Information Diffusion Models by Observed Data of Social Network” in Proceedings of the Advances in Social Computing, Third International Conference on Social Computing, Behavioral Modeling, and Prediction, pp. 149-158, Bethesda, MD, USA, March, 2010
    13. K. Saito, M. Kimura, and K. Ohara. “Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis” in Proceedings of the 1th Advances in Machine Learning, First Asian Conference on Machine Learning, ACML 2009, pp. 322-337, Nanjing, China, November, 2009
    14. P. Vasanthi, V. Tejaswi, and P. S. Thilagam. “Time Stamp Based Set Covering Greedy Algorithm” in Proceedings of the 21th ACM Ikdd Conference, pp. 110-111, Sydney, Australia, August, 2015
    15. B. Yang, J. Liu, and J. Feng. “On Modularity of Social Network Communities: The Spectral Characterization” in Proceedings of the 3th Ieee/wic/acm International Conference on Web Intelligence and Intelligent Agent Technology, 2008 Wi-Iat, pp. 127-133, Sydney, Australia, December, 2008
    16. W. Zheng, C.K. Wang, Z. Liu and J. M. Wang. “A Multi-Label Classification Algorithm Based on Random Walk Model,” Chinese Journal Of Computers, vol. 33, no. 8, pp. 1418-1426, August, 2010
    17. J. Zhou, Y. Zhang, and J. Cheng. “Preference-based Mining of Top-K Influential Nodes in Social Networks,” Future Generation Computer Systems, vol. 31, no.1, pp. 40-47, February, 2014



      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      This site uses encryption for transmitting your passwords.