Enhanced Leach Algorithm for Opinion Leader Detection in Social Networks Using Centrality Metrics
الموضوعات : journal of Artificial Intelligence in Electrical Engineering
یاسر علمی سولا
1
,
Afsaneh Razghandi
2
1 - دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه آزاد اسلامی، سبزوار ، ایران
2 -
الکلمات المفتاحية: Social Network Analysis, Opinion Leaders, Wireless Sensor Networks, Leach Algorithm, Centrality,
ملخص المقالة :
Today, the development of human communication through social networks has increased the importance of social network analysis. In social networks, the role of nodes is not the same and important nodes play a key role in network performance. The nodes which have more influence in a social network are called "opinion leaders". Measuring the importance of each node is needed in order to finding the opinion leaders of social networks. This parameter is measured using the centrality criteria in the network’s graph. This paper presents an enhanced version of the Leach algorithm, originally designed for wireless sensor networks, to identify opinion leaders in social networks. By modifying the selection criteria for cluster heads and incorporating centrality measures (degree, closeness, betweenness, eigenvector), the proposed approach demonstrates a significant correlation with established centrality metrics, achieving correlation values above 0.7. This advancement underscores the importance of node influence in social dynamics and offers a robust method for analyzing key actors in complex networks. In this paper, the well-known Leach algorithm that used to find the cluster heads in wireless sensor network has been modified to determine opinion leaders in social networks. The evaluation of the proposed algorithm is performed using centrality (degree, closeness, betweenness and eigenvector) indicators. Simulation results show that the proposed algorithm has a significant relationship with the standard centrality indices with a correlation value greater than 0.7.
1. Carrington PJ, Scott J, Wasserman S (2005) Models and methods in social network analysis, vol 28. Cambridge university press,
2. Benítez-Andrades JA, García-Rodríguez I, Benavides C, Alaiz-Moretón H, Rodríguez-González A (2020) Social network analysis for personalized characterization and risk assessment of alcohol use disorders in adolescents using semantic technologies. Future Generation Computer Systems 106:154-170
3. Pradhan T, Pal S (2020) A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity. Future Generation Computer Systems 110:1139-1166
4. Wei J, Meng F How opinion distortion appears in super-influencer dominated social network. Future Generation Computer Systems 115:542-552
5. Liu X, Zhu R, Anjum A, Wang J, Zhang H, Ma M (2020) Intelligent data fusion algorithm based on hybrid delay-aware adaptive clustering in wireless sensor networks. Future Generation Computer Systems 104:1-14
6. Fu C, Jiang Z, Wei W, Wei A (2013) An energy balanced algorithm of LEACH protocol in WSN. International Journal of Computer Science Issues (IJCSI) 10 (1):354
7. Wang T, Zeng J, Lai Y, Cai Y, Tian H, Chen Y, Wang B (2020) Data collection from WSNs to the cloud based on mobile Fog elements. Future Generation Computer Systems 105:864-872
8. Xu J, Jin N, Lou X, Peng T, Zhou Q, Chen Y Improvement of LEACH protocol for WSN. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012. IEEE, pp 2174-2177
9. Loscri V, Morabito G, Marano S A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In: IEEE vehicular technology conference, 2005. vol 3. IEEE; 1999, p 1809
10. Mahmood D, Javaid N, Mahmood S, Qureshi S, Memon AM, Zaman T MODLEACH: a variant of LEACH for WSNs. In: 2013 Eighth international conference on broadband and wireless computing, communication and applications, 2013. IEEE, pp 158-163
11. Rahmanian A, Omranpour H, Akbari M, Raahemifar K A novel genetic algorithm in LEACH-C routing protocol for sensor networks. In: 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE), 2011. IEEE, pp 001096-001100
12. Ahlawat A, Malik V An extended vice-cluster selection approach to improve v leach protocol in WSN. In: 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT), 2013. IEEE, pp 236-240
13. Okamoto K, Chen W, Li X-Y Ranking of closeness centrality for large-scale social networks. In: International workshop on frontiers in algorithmics, 2008. Springer, pp 186-195
14. Freeman LC, Roeder D, Mulholland RR (1979) Centrality in social networks: II. Experimental results. Social networks 2 (2):119-141
15. Elmi Sola Y, Pourjavad MA, Hasanpour H, Zojaji H, Analoui M Load balancing effects in DCUR QoS routing algorithm. In: 2009 2nd IEEE International Conference on Computer Science and Information Technology, 2009. IEEE, pp 210-213
16. Fumanal-Idocin J, Alonso-Betanzos A, Cordón O, Bustince H, Minárová M (2020) Community detection and social network analysis based on the Italian wars of the 15th century. Future Generation Computer Systems 113:25-40
17. Jensen D, Neville J (2003) Data mining in social networks. na,
18. Nettleton DF (2013) Data mining of social networks represented as graphs. Computer Science Review 7:1-34
19. Abdulsalam HM, Kamel LK W-LEACH: Weighted Low Energy Adaptive Clustering Hierarchy aggregation algorithm for data streams in wireless sensor networks. In: 2010 IEEE international conference on data mining workshops, 2010. IEEE, pp 1-8
20. Sasirekha S, Swamynathan S (2017) Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. Journal of Communications and Networks 19 (4):392-401
21. Kirsan AS, Al Rasyid UH, Syarif I, Purnamasari DN (2020) Energy Efficiency Optimization for Intermediate Node Selection Using MhSA-LEACH: Multi-hop Simulated Annealing in Wireless Sensor Network. EMITTER International Journal of Engineering Technology 8 (1):1-18
22. Baghouri M, Chakkor S, Hajraoui A (2014) Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks. arXiv preprint arXiv:14084112
