تشخیص انجمن های پایدار در شبکه های اجتماعی پویا با استفاده از گره های با نفوذ
محورهای موضوعی : مهندسی الکترونیک
طالب خفائی
1
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علیرضا توکلی طرقی
2
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مهدی حسین زاده
3
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علی رضائی
4
1 - گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران
2 - گروه علوم کامپیوتر، دانشکده ریاضیات، دانشگاه شهید بهشتی، تهران، ایران
3 - مرکز تحقیقات سلامت روان، پژوهشکده سلامت روان اجتماعی، دانشگاه علوم پزشکی ایران، تهران، ایران
علوم کامپیوتر، دانشگاه توسعه انسانی، سلیمانیه، عراق
4 - گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران، ایران
کلید واژه: گره های با اهمیت, انجمن های پایدار, معیار مرکزیت, شبکه های اجتماعی, انجمن ها,
چکیده مقاله :
شبکه های اجتماعی امروزه کاربردهای بسیاری در زندگی روزمره انسان ها پیدا کرده است به نحوه که شناسایی رفتار اعضای این نوع شبکه ها و انجمن های درون آنها از اهمیت ویژه ای برخوردار شده است. با توجه به ساختار و نحوه ارتباط بین اعضای شبکه های اجتماعی برخی اعضای درون این نوع شبکه ها نقش های مهمتری نسبت به دیگر اعضا دارند. در این مطالعه روشی جهت تشخیص انجمن های با اهمیت بیشتر پرداخته شد. باری این منظور با استفاده از ویژگیهای مرکزیت شبکه به معرفی ویژگیهای جدیدی پرداخته شد و سپس اهمیت این نوع ویژگیها توسط تئوری مجموعه های راف مورد بررسی قرار گرفت. نتایج آزمایش نشان داد که با افزایش تعداد گره های محبوب در بین یک انجمن که در این مطالعه معرفی شد و در عین حال کاهش مقدار ویژگی های تراکم، بینابینی و نزدیکی میزان تاثیر ویژگی تعداد گره های محبوب بر محبوب ماندن انجمن بیشتر مشهود خواهد بود.
Social networks have found many applications in people's daily lives today so that identifying the behavior of members of these types of networks and associations within them is of particular importance. Due to the structure and the way of communication between the members of social networks, some members within this type of network have more important roles than other members. In this study, a method for identifying more important communities was discussed. For this purpose, new features were introduced using network centralization features and then the importance of this type of feature was investigated by Rough set theory. The experimental results showed that by increasing the number of popular nodes among a community where introduced in this study and at the same time decreasing the value of density, betweenness, and closeness centrality features of that community, it causes the effect of the number of popular nodes on the community will remain more evident.
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[1] V. Martínez, F. Berzal, and J.-C. Cubero, “A Survey of Link Prediction in Complex Networks,” ACM Computing Surveys (CSUR), vol. 49, no. 4, pp. 69, 2016, doi:10.1145/3012704.
[2] P. Symeonidis, E. Tiakas, and Y. Manolopoulos, "Transitive node similarity for link prediction in social networks with positive and negative links," Proceedings of the ACM Conference on Recommender Systems, 2010, pp. 183-190, ,doi:10.1145/1864708.1864744.
[3] B. Gliwa, P. Bródka, A. Zygmunt, S. Saganowski, P. Kazienko and J. Koźlak, "Different approaches to community evolution prediction in blogosphere," IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2013, pp. 1291-1298, doi: 10.1145/2492517.2500231.
[4] K. S. Xu, and A. O. Hero, “Dynamic stochastic blockmodels for time-evolving social networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 4, pp. 552-562, 2014, doi: 10.1109/JSTSP.2014.2310294.
[5] S. Ahajjam, M. El Haddad, and H. Badir, “A new scalable leader-community detection approach for community detection in social networks,” Social Networks, vol. 54, pp. 41-49, 2018, doi:10.1016/j.socnet.2017.11.004.
[6] S. Aghaalizadeh, S. T. Afshord, A. Bouyer, and B. Anari, “A three-stage algorithm for local community detection based on the high node importance ranking in social networks,” Physica A: Statistical Mechanics and its Applications, vol. 563, pp. 125420, 2021, doi:10.1016/j.physa.2020.125420.
[7] G. He, J. Luo, and M. Yin, "An Evaluation Algorithm of the Importance of Network Node Based on Community Influence." International Conference DMBD, 2020, pp. 57-70, doi:10.1007/978-981-15-7205-0_6.
[8] J. Li, T. Cai, K. Deng, X. Wang, T. Sellis, and F. Xia, “Community-diversified influence maximization in social networks,” Information Systems, vol. 92, pp. 101522, 2020, doi:10.1016/j.is.2020.101522.
[9] Z. Zhang, X. Li, and C. Gan, “Identifying influential nodes in social networks via community structure and influence distribution difference,” Digital Communications and Networks, vol. 7, no. 1, pp. 131-139, 2021, doi:10.1016/j.dcan.2020.04.011.
[10] A. Salavaty, M. Ramialison, and P. D. Currie, “Integrated value of influence: an integrative method for the identification of the most influential nodes within networks,” Patterns, vol. 1, no. 5, pp. 100052, 2020, doi:10.1016/j.patter.2020.100052.
[11] F. Morone, B. Min, L. Bo, R. Mari, and H. A. Makse, “Collective influence algorithm to find influencers via optimal percolation in massively large social media,” Scientific reports, vol. 6, no. 1, pp. 1-11, 2016, doi: 10.1038/srep30062.
[12] M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse, “Identification of influential spreaders in complex networks,” Nature physics, vol. 6, no. 11, pp. 888-893, 2010, doi:10.1038/nphys1746.
[13] B. Bollobás, Graph Theory and Combinatorics: Proceedings of the Cambridge Combinatorial Conference in Honour of Paul Erdös,[Trinity College, Cambridge, 21-25 March 1983]: Academic Press, 1984.
[14] S. B. Seidman, “Network structure and minimum degree,” Social networks, vol. 5, no. 3, pp. 269-287, 1983, doi:10.1016/0378-8733(83)90028-X.
[15] S. Carmi, S. Havlin, S. Kirkpatrick, Y. Shavitt, and E. Shir, “A model of Internet topology using k-shell decomposition,” Proceedings of the National Academy of Sciences, vol. 104, no. 27, pp. 1-1150-1154, 2007, doi:10.1073/pnas.0701175104.
