دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات13
تعداد شماره‌ها200
تعداد مقالات2,008
تعداد مشاهده مقاله2,679,882
تعداد دریافت فایل اصل مقاله1,937,232
Sabzekar, M., Baradaran Nejad, S., Khazaeipoor, M.. (1402). A Node-Centric Approach for Community Detection in Dynamic Networks. فناوری آموزش, (), 305-318. doi: 10.22061/jecei.2024.10202.687
M. Sabzekar; S. Baradaran Nejad; M. Khazaeipoor. "A Node-Centric Approach for Community Detection in Dynamic Networks". فناوری آموزش, , , 1402, 305-318. doi: 10.22061/jecei.2024.10202.687
Sabzekar, M., Baradaran Nejad, S., Khazaeipoor, M.. (1402). 'A Node-Centric Approach for Community Detection in Dynamic Networks', فناوری آموزش, (), pp. 305-318. doi: 10.22061/jecei.2024.10202.687
Sabzekar, M., Baradaran Nejad, S., Khazaeipoor, M.. A Node-Centric Approach for Community Detection in Dynamic Networks. فناوری آموزش, 1402; (): 305-318. doi: 10.22061/jecei.2024.10202.687

A Node-Centric Approach for Community Detection in Dynamic Networks

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 25 دی 1402
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10202.687
نویسندگان
M. Sabzekar* 1؛ S. Baradaran Nejad2؛ M. Khazaeipoor2
1Department of Computer Engineering, Birjand University of Technology, Birjand, Iran.
2Department of Computer Engineering, Birjand Branch, Islamic Azad University, Birjand, Iran.
تاریخ دریافت: 03 مهر 1402،  تاریخ بازنگری: 13 دی 1402،  تاریخ پذیرش: 25 دی 1402 
چکیده
Background and Objectives: Nowadays, social networks are recognized as significant sources of information exchange. Consequently, many organizations have chosen social networks as essential tools for marketing and brand management. Communities are essential structures that can enhance the performance of social networks by grouping nodes and analyzing the information derived from them. This subject becomes more important with the increase in information volume and the complexity of relationships in networks. The goal of community identification is to find subgraphs that are densely connected internally but loosely connected externally.
Methods: While community detection has mostly been studied in static networks in the past, this paper focuses on dynamic networks and the influence of central nodes in forming communities. In the proposed algorithm, the network is captured through multiple snapshots. The initial snapshot calculates the influence of each node. Then, by selecting k nodes with higher influence, network communities are formed, and other nodes belong to the community with the most common edges. In the second step, after receiving the next snapshot, communities are updated. Then, k nodes with higher influence are selected, and their associated community is created if needed. If the previous community centers are not among the newly selected k nodes, the community is dissolved, and the nodes within it belong to other communities.
Results: Based on the results obtained, the proposed algorithm has managed to achieve better results in most cases compared to the compared algorithms, especially in terms of modularity metrics. The reason behind this success could be attributed to the utilization of influential nodes in community formation.
Conclusion: Drawing from the outcomes attained, the suggested algorithm has effectively outperformed the contrasted algorithms in a majority of instances, particularly concerning metrics related to modularity. This accomplishment can potentially be ascribed to the incorporation of influential nodes during the process of community formation.
کلیدواژه‌ها
Social networks؛ Dynamic networks؛ Community detection؛ Node influence؛ Overlapping communities
مراجع

 [1] B. Yang, D. Liu, J. Liu, “Discovering communities from social networks: Methodologies and applications,” Handb. Soc. Netw. Technol. Appl.: 331–346, 2010.

[2] S. Souravlas, S. D. Anastasiadou, T. Economides, S. Katsavounis, “Probabilistic community detection in social networks,” IEEE Access, 11: 25629–25641, 2023.

[3] A. Abbasi, H. Chen, A. Salem, “Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums,” ACM Trans. Inf. Syst., 26(3): 1–34, 2008.

[4] R. K. Bakshi, N. Kaur, R. Kaur, G. Kaur, “Opinion mining and sentiment analysis,” in Proc. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 16: 452-455, 2016.

[5] T. Ma, Q. Liu, J. Cao, Y. Tian, A. Al-Dhelaan, M. Al-Rodhaan, “LGIEM: Global and local node influence-based community detection,” Futur. Gener. Comput. Syst., 105: 533–546, 2020.

[6] N. Chen, Y. Liu, J. Cheng, Q. Liu, “A novel parallel community detection scheme based on label propagation,” World Wide Web, 21: 1377–1398, 2018.

[7] Y. Niu, D. Kong, L. Liu, R. Wen, J. Xiao, “Overlapping community detection with adaptive density peaks clustering and iterative partition strategy,” Expert Syst. Appl., 213: 119213, 2023.

[8] A. Reihanian, M. R. Feizi-Derakhshi, H. S. Aghdasi, “An enhanced multi-objective biogeography-based optimization for overlapping community detection in social networks with node attributes,” Inf. Sci. (Ny)., 622: 903-929, 2023.

[9] P. Agarwal, R. Verma, A. Agarwal, T. Chakraborty, “DyPerm: Maximizing permanence for dynamic community detection,” in Proc. Pacific-Asia conference on knowledge discovery and data mining: 437-449, 2018.

[10] X. Zeng, W. Wang, C. Chen, G. G. Yen, “A consensus community-based particle swarm optimization for dynamic community detection,” IEEE Trans. Cybern., 50(6): 2502–2513, 2020.

[11] S. Ahajjam, M. El Haddad, H. Badir, “A new scalable leader-community detection approach for community detection in social networks,” Soc. Networks, 54: 41-49, 2018.

[12] K. Dasgupta et al., “Social ties and their relevance to churn in mobile telecom networks,” in Proc. 11th international conference on Extending database technology: Advances in database technology: 668–677, 2008

[13] M. A. Al-Garadi et al., “Analysis of online social network connections for identification of influential users: Survey and open research issues,” ACM Comput. Surv., 51(1): 1-37, 2018.

[14] Y. Zhao, “A survey on theoretical advances of community detection in networks,” Wiley Interdiscip. Rev. Comput. Stat., 9(5): 1403, 2017.

[15] S. Bahadori, H. Zare, P. Moradi, “PODCD: Probabilistic overlapping dynamic community detection,” Expert Syst. Appl., 174: 114650, 2021.

[16] N. Chen, B. Hu, Y. Rui, “Dynamic network community detection with coherent neighborhood propinquity,” IEEE Access, 8: 27915-27926, 2020.

[17] B. S. Khan, M. A. Niazi, “Network community detection: A review and visual survey,” arXiv Prepr. arXiv1708.00977, 2017.

[18] R. Cazabet, G. Rossetti, F. Amblard, “Dynamic community detection,” In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY., 2017.

[19] G. Rossetti, R. Cazabet, “Community discovery in dynamic networks: a survey,” ACM Comput. Surv., 51(2): 1-37, 2018.

[20] S. Souravlas, S. Anastasiadou, S. Katsavounis, “A survey on the recent advances of deep community detection,” Appl. Sci., 11(16): 7179, 2021.

[21] D. Zhuang, J. M. Chang, M. Li, “DynaMo: Dynamic community detection by incrementally maximizing modularity,” IEEE Trans. Knowl. Data Eng., 33(5): 1934-1945, 2019.

[22] M. A. Javed, M. S. Younis, S. Latif, J. Qadir, A. Baig, “Community detection in networks: A multidisciplinary review,” J. Netw. Comput. Appl., 108: 87-111, 2018.

[23] J. Scripps, “Discovering influential nodes in social networks through community finding.,” in Proc. WEBIST: 403-412, 2013.

[24] J. Dai et al., “Identifying influential nodes in complex networks based on local neighbor contribution,” IEEE Access, 7: 131719-131731, 2019.

[25] X. K. Zhang, J. Ren, C. Song, J. Jia, Q. Zhang, “Label propagation algorithm for community detection based on node importance and label influence,” Phys. Lett. A, 381(33): 2691-2698, 2017.

[26] C. Li et al., “NANI: an efficient community detection algorithm based on nested aggregation of node influence,” in Proc. the ACM Turing 50th Celebration Conference-China: 1-10, 2017.

[27] M. Cordeiro, R. P. Sarmento, J. Gama, “Dynamic community detection in evolving networks using locality modularity optimization,” Soc. Netw. Anal. Min., 6: 1-20, 2016.

[28] W. Li, X. Zhou, C. Yang, Y. Fan, Z. Wang, Y. Liu, “Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery,” Information Fusion, 79: 110–123, 2022.

[29] Q. Ni, J. Guo, W. Wu H. Wang, “Influence-Based community partition with sandwich method for social networks,” IEEE Trans. Comput. Social Syst., 10: 2: 819-830, 2023.

[30] H. Long, X. Li, X. Liu, X. et al., “BBTA: Detecting communities incrementally from dynamic networks based on tracking of backbones and bridges,” Applied Intelligence, 53:1084–1100, 2023.

[31] S. A. Seyedi, A. Lotfi, P. Moradi, N. N. Qader, “Dynamic graph-based label propagation for density peaks clustering,” Expert Syst. Appl., 115: 314–328, 2019.

[32] F. Liu, J. Wu, C. Zhou, J. Yang, “Evolutionary community detection in dynamic social networks,” in Proc. 2019 International Joint Conference on Neural Networks (IJCNN): 1–7, 2019.

[33] X. Su, J. Cheng, H. Yang, M. Leng, W. Zhang, X. Chen, “IncNSA: Detecting communities incrementally from time-evolving networks based on node similarity,” Int. J. Mod. Phys. C, 31(7): 2050094, 2020.

 

آمار
تعداد مشاهده مقاله: 205


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب