Detection of Communities on Social Networks Based on Label Propagation Algorithm and Fuzzy Methods
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
Mohsen Chekin
1
,
Amin Mehranzadeh
2
1 - Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.
2 - Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.
کلید واژه: Virtual Communities, Social Networks, Label Propagation Algorithm, Fuzzy Delphi Method.,
چکیده مقاله :
The proliferation of the web and social networks has made people more connected to their friends and neighbors than ever before. The desire of individuals to relate to similar tastes and choices in a social network leads to the formation of clusters or virtual communities. Such information can be useful for commercial, educational or developmental purposes and therefore a large number of algorithms for detecting communities have been presented. There are many algorithms for detecting communities on social networks. In this paper, using the label propagation algorithm and fuzzy Delphi method, an improved method is presented that can identify communities more accurately and quickly than other similar methods. Accordingly, in the proposed algorithm, instead of randomly selecting from the maximum labels of the neighboring nodes, the label with the highest weight is chosen. By doing this, random selection is eliminated, and stability and certainty in the outcomes of the algorithm are achieved.
The proliferation of the web and social networks has made people more connected to their friends and neighbors than ever before. The desire of individuals to relate to similar tastes and choices in a social network leads to the formation of clusters or virtual communities. Such information can be useful for commercial, educational or developmental purposes and therefore a large number of algorithms for detecting communities have been presented. There are many algorithms for detecting communities on social networks. In this paper, using the label propagation algorithm and fuzzy Delphi method, an improved method is presented that can identify communities more accurately and quickly than other similar methods. Accordingly, in the proposed algorithm, instead of randomly selecting from the maximum labels of the neighboring nodes, the label with the highest weight is chosen. By doing this, random selection is eliminated, and stability and certainty in the outcomes of the algorithm are achieved.
[1] P. Bedi and TCh. Sharma, Community detection in social networks, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(3) (2016), 115-135.
[2] R. Campigott, P.C. Céspedes and J-L. Guillaume, A generalized and adaptive method for community detection, arXiv preprint arXiv:1406.2518, (2014).
[3] S. Cao and J. He, A label propagation algorithm based on similarity and weighted, in 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), (2020), 864-870.
[4] H. S. Dhiman and D. Deb, Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms, Energy, (202) (2020), 117-755.
[5] Y. Du, T. Li, M.S. Pathan, H. K. Teklehaimanot and Zh. Yang, An effective sarcasm detection approach based on sentimental context and individual expression habits, Cognitive Computation, 14(1) (2022), 78-90.
[6] E. Jokar and M. Mosleh, Community detection in social networks based on improved label propagation algorithm and balanced link density, Physics Letters A, 383(8) (2019), 718-727.
[7] J. Khodadoust, A.M. Khodadoust, S.S. Mirkamali and S. Ayat, Fingerprint indexing for wrinkled fingertips immersed in liquids, Expert Systems with Applications, 146 (2020), 113-153.
[8] I. D. Luptakova and J. Pospichal, Community Detection Based Clustering, IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), (2018), 289-294.
[9] I. Messaoudi and N. Kamel, A multi-objective bat algorithm for community detection on dynamic social networks, Applied Intelligence, 49(6) (2019), 2119-2136.
[10] S. Papadopoulos, Y. Kompatsiaris, A. Vakali and P. Spyridonos, Community detection in social media, Data mining and knowledge discovery, 24(3) (2012), 515-554.
[11] C. Pizzuti, Ga-net: A genetic algorithm for community detection in social networks, International conference on parallel problem solving from nature, (2008), 1081-1090.
[12] N. Shahabi Sani, M. Manthouri, and F. Farivar, A multi-objective ant colony optimization algorithm for community detection in complex networks, Journal of Ambient Intelligence and Humanized Computing, 11(1) (2020), 5-21.
[13] J. Sheng, Q. Li, B. Wang, W. Guan, J. Dai, J. Hu, and H. Zuo, Node trust: an effective method to detect non-overlapping community in social networks, Modern Physics Letters B, 35(1) (2021), 2150036.
[14] G. Suryateja and S. Palani, Survey on efficient community detection in social networks, international conference on intelligent sustainable systems (ICISS) ,(2017), 93-97.
[15] Y. Xing, F. Meng, Y. Zhou, M. Zhu, M. Shi, and G. Sun, A node influence based label propagation algorithm for community detection in networks, The Scientific World Journal, (2014).
[16] G. Yang, W. Zheng, C. Che, and W. Wang, Graph-based label propagation algorithm for community detection, International Journal of Machine Learning and Cybernetics, 11(6) (2020), 1319-1329.
[17] X. You, Y. Ma, and Z. Liu, A three-stage algorithm on community detection in social networks, Knowledge-Based Systems, (187) (2020), 104822.
[18] X.-K. Zhang, X. Tian, Y.-N. Li, and C. Song, Label propagation algorithm based on edge clustering coefficient for community detection in complex networks, International Journal of Modern Physics B, 28(30) (2014), 145-216
