Full Paper View Go Back
Using Cuckoo Optimization Algorithm to Identify Communities in Social Networks
Amin Rezaeipanah1 , Mousa Mojarad2 , Seyed Mohammad Fazel Hosseini3
Section:Research Paper, Product Type: Journal-Paper
Vol.6 ,
Issue.6 , pp.113-119, Dec-2019
Online published on Dec 31, 2019
Copyright © Amin Rezaeipanah, Mousa Mojarad, Seyed Mohammad Fazel Hosseini . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
View this paper at Google Scholar | DPI Digital Library
How to Cite this Paper
- IEEE Citation
- MLA Citation
- APA Citation
- BibTex Citation
- RIS Citation
IEEE Style Citation: Amin Rezaeipanah, Mousa Mojarad, Seyed Mohammad Fazel Hosseini, “Using Cuckoo Optimization Algorithm to Identify Communities in Social Networks,” International Journal of Scientific Research in Biological Sciences, Vol.6, Issue.6, pp.113-119, 2019.
MLA Style Citation: Amin Rezaeipanah, Mousa Mojarad, Seyed Mohammad Fazel Hosseini "Using Cuckoo Optimization Algorithm to Identify Communities in Social Networks." International Journal of Scientific Research in Biological Sciences 6.6 (2019): 113-119.
APA Style Citation: Amin Rezaeipanah, Mousa Mojarad, Seyed Mohammad Fazel Hosseini, (2019). Using Cuckoo Optimization Algorithm to Identify Communities in Social Networks. International Journal of Scientific Research in Biological Sciences, 6(6), 113-119.
BibTex Style Citation:
@article{Rezaeipanah_2019,
author = {Amin Rezaeipanah, Mousa Mojarad, Seyed Mohammad Fazel Hosseini},
title = {Using Cuckoo Optimization Algorithm to Identify Communities in Social Networks},
journal = {International Journal of Scientific Research in Biological Sciences},
issue_date = {12 2019},
volume = {6},
Issue = {6},
month = {12},
year = {2019},
issn = {2347-2693},
pages = {113-119},
url = {https://www.isroset.org/journal/IJSRBS/full_paper_view.php?paper_id=1618},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.isroset.org/journal/IJSRBS/full_paper_view.php?paper_id=1618
TI - Using Cuckoo Optimization Algorithm to Identify Communities in Social Networks
T2 - International Journal of Scientific Research in Biological Sciences
AU - Amin Rezaeipanah, Mousa Mojarad, Seyed Mohammad Fazel Hosseini
PY - 2019
DA - 2019/12/31
PB - IJCSE, Indore, INDIA
SP - 113-119
IS - 6
VL - 6
SN - 2347-2693
ER -
Abstract :
Social network analysis helps discover communities and interactions between users. A community is a group of users with high communication density in that group. Although many algorithms have been developed to identify communities, most are inefficient in terms of processing time and cost for large-scale social networks. In this research, we present a simple and efficient algorithm for social recognition in social networks that does not require any prior knowledge about the number of network communities. Most existing methods of community recognition examine the structure of the social network graph without considering issues and interactions between users. In the proposed method, in addition to considering the communication topology between users, we also consider the tweets used by them. The proposed system consists of three general steps. In the first step, the similarity between each user pair is calculated on the basis of a hybrid clustering method. Initial assemblages are based on the similarity matrix in the second step. Finally, in the third stage, using the cuckoo optimization algorithm, the initial clusters are combined and the final clusters are created. In the cuckoo algorithm, the performance of each solution is evaluated using the metrics evaluation criterion on Twitter`s social network. The results show better performance of the proposed method in different criteria than CC-GA and MDCL methods.
Key-Words / Index Term :
Clustering, Cuckoo Search Algorithm, Community Identification, Social Networks
References :
[1] He, K., Li, Y., Soundarajan, S., & Hopcroft, J. E. Hidden community detection in social networks. Information Sciences, 425, 92-106, 2018
[2] Ferreira, L. N., & Zhao, L. Time series clustering via community detection in networks. Information Sciences, 326, 227-242, 2016
[3] Saida, I. B., Nadjet, K., & Omar, B. A new algorithm for data clustering based on cuckoo search optimization. In Genetic and Evolutionary Computing (pp. 55-64). Springer, Cham, 2014
[4] Cobos, C., Muñoz-Collazos, H., Urbano-Muñoz, R., Mendoza, M., León, E., & Herrera-Viedma, E. Clustering of web search results based on the cuckoo search algorithm and balanced Bayesian information criterion. Information Sciences, 281, 248-264, 2014
[5] Nazir, S., Shafiq, S., Iqbal, Z., Zeeshan, M., Tariq, S., & Javaid, N. Cuckoo Optimization Algorithm Based Job Scheduling Using Cloud and Fog Computing in Smart Grid. In International Conference on Intelligent Networking and Collaborative Systems (pp. 34-46). Springer, Cham. September 2018
[6] Cui, Z., Sun, B., Wang, G., Xue, Y., & Chen, J. A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber–physical systems. Journal of Parallel and Distributed Computing, 103, 2017
[7] Serrat, O. Social network analysis. In Knowledge solutions (pp. 39-43). Springer, Singapore. 2017
[8] Reihanian, A., Feizi-Derakhshi, M. R., & Aghdasi, H. S. Community detection in social networks with node attributes based on multi-objective biogeography based optimization. Engineering Applications of Artificial Intelligence, 62, 51-67, 2017
[9] Araujo, M., Papadimitriou, S., Günnemann, S., Faloutsos, C., Basu, P., Swami, A., ... & Koutra, D. Com2: fast automatic discovery of temporal (‘comet’) communities. InPacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 271-283). Springer, Cham. 2014, May
[10] Hao, F., Min, G., Pei, Z., Park, D. S., & Yang, L. T. Clique Community Detection in Social Networks Based on Formal Concept Analysis. IEEE Systems Journal, 11(1), 250-259, 2017
[11] Fani, H., & Bagheri, E. Community detection in social networks. Encyclopedia with Semantic Computing and Robotic Intelligence, 1(1), 2017
[12] Devi, J. C., & Poovammal, E. An Analysis of Overlapping Community Detection Algorithms in Social Networks. Procedia Computer Science,89, 349-358, 2016
[13] Kordestani, J. K., Firouzjaee, H. A., & Meybodi, M. R. An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems. Applied Intelligence, 48(1), 97-117, 2018
[14] Kaur, S., Singh, S., Kaushal, S., & Sangaiah, A. K. Comparative analysis of quality metrics for community detection in social networks using genetic algorithm. Neural Network World, 26(6), 625, 2016
[15] Xu, C., Zhang, H., Lu, B., & Wu, S. Local Community Detection Using Social Relations and Topic Features in Social Networks. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (pp. 371-383). Springer, Cham, 2017
[16] Anand, K., Kumar, J., & Anand, K. Anomaly detection in online social network: A survey. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on (pp. 456-459). IEEE, March 2017
[17] Moosavi, S. A., Jalali, M., Misaghian, N., Shamshirband, S., & Anisi, M. H. (). Community detection in social networks using user frequent pattern mining. Knowledge and Information Systems, 51(1), 159-186, 2017
[18] Habashi, S., Ghanem, N. M., & Ismail, M. A. Enhanced community detection in social networks using active spectral clustering. InProceedings of the 31st Annual ACM Symposium on Applied Computing (pp. 1178-1181). ACM, April 2016
[19] Zhou, X., Liu, Y., Li, B., & Li, H. A multiobjective discrete cuckoo search algorithm for community detection in dynamic networks. Soft Computing, 21(22), 6641-6652, 2017
You do not have rights to view the full text article.
Please contact administration for subscription to Journal or individual article.
Mail us at support@isroset.org or view contact page for more details.