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Reseach Article

Semantic Topics Modeling Approach for Community Detection

by Haasan Abdelbary, Abeer El-korany
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 6
Year of Publication: 2013
Authors: Haasan Abdelbary, Abeer El-korany
10.5120/14020-2177

Haasan Abdelbary, Abeer El-korany . Semantic Topics Modeling Approach for Community Detection. International Journal of Computer Applications. 81, 6 ( November 2013), 50-58. DOI=10.5120/14020-2177

@article{ 10.5120/14020-2177,
author = { Haasan Abdelbary, Abeer El-korany },
title = { Semantic Topics Modeling Approach for Community Detection },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 6 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 50-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number6/14020-2177/ },
doi = { 10.5120/14020-2177 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:25.039473+05:30
%A Haasan Abdelbary
%A Abeer El-korany
%T Semantic Topics Modeling Approach for Community Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 6
%P 50-58
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social networks play an increasingly important role in online world as it enables individuals to easily share opinions, experiences and expertise. The capability to extract latent communities based on user interest is becoming vital for a wide variety of applications. However, existing literature on community extraction has largely focused on methods based on the link structure of a given social network. Such link-based methods ignore the content of social interactions, which may be crucial for accurate and meaningful community extraction. In this paper, we present a novel approach for community extraction which naturally incorporates the content published within the social network with its semantic features. Two layer generative Restricted Boltzmann Machines model is applied for community discovery. The model assumes that users within a community communicate based on topics of mutual interest. The proposed model naturally allows users to belong to multiple communities. Through extensive experiments on the Twitter data for scientific papers, we demonstrate that the model is able to extract well-connected and topically meaningful communities.

References
  1. Bradford, R. (2006). Application of latent semantic indexing in generating graphs of terrorist networks. Intelligence and Security Informatics, 674-675.
  2. Devi R, R. (2013). A Perspective Analysis of Hidden Community Mining Methods in Large Scale Social Networks. International Journal of Computer Applications, 75(3), 7-12.
  3. El-Korany, A. (2012). Society in Hand: Toward Community Service through Social Network. International Journal of Computer Applications, 51(8), 15-22.
  4. Hinton, G. E. , Osindero, S. , & Teh, Y. -W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7), 1527-1554.
  5. Larochelle, H. , Erhan, D. , Courville, A. , Bergstra, J. , & Bengio, Y. (2007). An empirical evaluation of deep architectures on problems with many factors of variation. Paper presented at the Proceedings of the 24th international conference on Machine learning.
  6. Larochelle, H. , Mandel, M. , Pascanu, R. , & Bengio, Y. (2012). Learning Algorithms for the Classification Restricted Boltzmann Machine. The Journal of Machine Learning Research, 13, 643-669.
  7. Li, Y. , Bandar, Z. A. , & McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. Knowledge and Data Engineering, IEEE Transactions on, 15(4), 871-882.
  8. Lin, C. -Y. , Koh, J. -L. , & Chen, A. (2010). A better strategy of discovering link-pattern based communities by classical clustering methods. Advances in Knowledge Discovery and Data Mining, 56-67.
  9. Liu, J. , Liu, F. , Zhou, J. , & He, C. (2009). Irregular Community Discovery for Social CRM in Cloud Computing. Cloud Computing, 497-509.
  10. Mnih, A. , & Hinton, G. (2007). Three new graphical models for statistical language modelling. Paper presented at the Proceedings of the 24th international conference on Machine learning.
  11. Pathak, N. , DeLong, C. , Banerjee, A. , & Erickson, K. (2008). Social topic models for community extraction. Paper presented at the The 2nd SNA-KDD Workshop.
  12. Pizzuti, C. (2008). GA-Net: A genetic algorithm for community detection in social networks. Parallel Problem Solving from Nature–PPSN X, 1081-1090.
  13. Prakash, D. , &Surendran, S. (2013). Detection and Analysis of Hidden Activities in Social Networks. International Journal of Computer Applications,77(16), 34-38.
  14. Sachan, M. , Contractor, D. , Faruquie, T. A. , & Subramaniam, L. V. (2012). Using content and interactions for discovering communities in social networks. Paper presented at the Proceedings of the 21st international conference on World Wide Web.
  15. Salakhutdinov, R. , Mnih, A. , & Hinton, G. (2007). Restricted Boltzmann machines for collaborative filtering. Paper presented at the ACM international conference proceeding series.
  16. Tang, J. , & Zhang, J. (2009). A discriminative approach to Topic-Based citation recommendation. Advances in Knowledge Discovery and Data Mining, 572-579.
  17. Tang, J. , Zhang, J. , Yu, J. X. , Yang, Z. , Cai, K. , Ma, R. , . . . Su, Z. (2009). Topic distributions over links on web. Paper presented at the Data Mining, 2009. ICDM'09. Ninth IEEE International Conference on.
  18. Wan, H. -Y. , Lin, Y. -F. , Wu, Z. -H. , & Huang, H. -K. (2012). Discovering Typed Communities in Mobile Social Networks. Journal of Computer Science and Technology, 27(3), 480-491.
  19. Welling, M. , Rosen-Zvi, M. , & Hinton, G. (2005). Exponential family harmoniums with an application to information retrieval. Advances in neural information processing systems, 17, 1481-1488.
  20. Wu, Z. , & Palmer, M. (1994). Verbs semantics and lexical selection. Paper presented at the Proceedings of the 32nd annual meeting on Association for Computational Linguistics.
  21. Xie, J. , & Szymanski, B. (2012). Towards linear time overlapping community detection in social networks. Advances in Knowledge Discovery and Data Mining, 25-36.
  22. Xu, K. , Kliger, M. , & Hero, A. (2011). Tracking communities in dynamic social networks. Social Computing, Behavioral-Cultural Modeling and Prediction, 219-226.
  23. Yang, L. , Liu, F. , Kizza, J. M. , & Ege, R. K. (2009). Discovering topics from dark websites. Paper presented at the Computational Intelligence in Cyber Security, 2009. CICS'09. IEEE Symposium on.
  24. Yu, S. , & Kak, S. (2012). A Survey of Prediction Using Social Media. arXiv preprint arXiv:1203. 1647.
Index Terms

Computer Science
Information Sciences

Keywords

Social network Community discovery Machine learning Restricted Boltzmann Machines Topic modeling Semantic similarity.