Papers
Topics
Authors
Recent
2000 character limit reached

Twotier -- A Layered Analysis of Backbone Members in a Moderate Sized Community Sports Organization (2307.04118v1)

Published 9 Jul 2023 in cs.SI

Abstract: Backbone members are recognized as essential parts of an organization, yet their role and mechanisms of functioning in networks are not fully understood. In this paper, we propose a new framework called Twotier to analyze the evolution of community sports organizations (CSOs) and the role of backbone members. Tier-one establishes a dynamic user interaction network based on grouping relationships, and weighted k-shell decomposition is used to select backbone members. We perform community detection and capture the evolution of two separate sub-networks: one formed by backbone members and the other formed by other members. In Tier-two, the sub-networks are abstracted, revealing a core-periphery structure in the organization where backbone members serve as bridges connecting all parts of the network. Our findings suggest that relying on backbone members can keep newcomers actively involved in rewarding activities, while non-rewarding activities solidify relations between backbone members.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. K. Misener and A. Doherty, “In support of sport: Examining the relationship between community sport organizations and sponsors,” Sport Management Review, vol. 17, no. 4, pp. 493–506, 2014.
  2. K. Van der Veken, E. Lauwerier, and S. J. Willems, “How community sport programs may improve the health of vulnerable population groups: a program theory,” International journal for equity in health, vol. 19, no. 1, pp. 1–12, 2020.
  3. K. Westberg, C. Stavros, L. Parker, A. Powell, D. M. Martin, A. Worsley, M. Reid, and D. Fouvy, “Promoting healthy eating in the community sport setting: A scoping review,” Health Promotion International, vol. 37, no. 1, p. daab030, 2022.
  4. A. Doherty and G. Cuskelly, “Organizational capacity and performance of community sport clubs,” Journal of Sport Management, vol. 34, no. 3, pp. 240–259, 2019.
  5. J. Zhu, Y. Liu, and X. Yin, “A new structure-hole-based algorithm for influence maximization in large online social networks,” IEEE Access, vol. 5, pp. 23 405–23 412, 2017.
  6. M. Kerlund, “The importance of influential users in (re)producing swedish far-right discourse on twitter:,” European Journal of Communication, vol. 35, no. 6, pp. 613–628, 2020.
  7. Z. Zhao, “Propagation structure feature of entertainment news in the weibo online social network,” EPL (Europhysics Letters), vol. 135, no. 1, p. 16002, 2021.
  8. K. Zhao, J. Yen, G. Greer, B. Qiu, P. Mitra, and K. Portier, “Finding influential users of online health communities: a new metric based on sentiment influence,” Journal of the American Medical Informatics Association, vol. 21, no. e2, pp. e212–e218, 2014.
  9. L. Lü, D. Chen, X.-L. Ren, Q.-M. Zhang, Y.-C. Zhang, and T. Zhou, “Vital nodes identification in complex networks,” Physics Reports, vol. 650, pp. 1–63, 2016.
  10. P. De Meo, M. Levene, F. Messina, and A. Provetti, “A general centrality framework-based on node navigability,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 11, pp. 2088–2100, 2019.
  11. P.-D. Yu, C. W. Tan, and H.-L. Fu, “Epidemic source detection in contact tracing networks: Epidemic centrality in graphs and message-passing algorithms,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 2, pp. 234–249, 2022.
  12. D. Santoro and I. Sarpe, “Onbra: Rigorous estimation of the temporal betweenness centrality in temporal networks,” in Proceedings of the ACM Web Conference 2022, 2022, pp. 1579–1588.
  13. K. Das, S. Samanta, and M. Pal, “Study on centrality measures in social networks: a survey,” Social network analysis and mining, vol. 8, no. 1, pp. 1–11, 2018.
  14. M. P. Rombach, M. A. Porter, J. H. Fowler, and P. J. Mucha, “Core-periphery structure in networks,” SIAM Journal on Applied mathematics, vol. 74, no. 1, pp. 167–190, 2014.
  15. 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.
  16. A. Garas, F. Schweitzer, and S. Havlin, “A k-shell decomposition method for weighted networks,” New Journal of Physics, vol. 14, no. 8, p. 083030, 2012.
  17. Q. Zhou, J. Yu, and W. Sun, “Formation of a community: in the case of a particular non-profit sports organization,” in 2020 International Conference on Computing, Networking and Communications (ICNC).   IEEE, 2020, pp. 844–848.
  18. J. Yu, M. Ding, Q. Wang, W. Sun, and W. Hu, “Community sports organization development from a social network evolution perspective–structures, stages, and stimulus,” IEEE Transactions on Computational Social Systems, 2021.
  19. M. Z. Shafiq, M. U. Ilyas, A. X. Liu, and H. Radha, “Identifying leaders and followers in online social networks,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. 618–628, 2013.
  20. M. Q. Ott, J. M. Light, M. A. Clark, and N. P. Barnett, “Strategic players for identifying optimal social network intervention subjects,” Social networks, vol. 55, pp. 97–103, 2018.
  21. P. Bródka, S. Saganowski, and P. Kazienko, “GED: the method for group evolution discovery in social networks,” Social Network Analysis and Mining, vol. 3, no. 1, pp. 1–14, 2013.
  22. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” Journal of statistical mechanics: theory and experiment, vol. 2008, no. 10, p. P10008, 2008.
  23. H. Kwak, Y. Choi, Y. H. Eom, H. Jeong, and S. B. Moon, “Mining communities in networks: A solution for consistency and its evaluation,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement 2009, Chicago, Illinois, USA, November 4-6, 2009, 2009.
  24. X. Wang, Y. Xing, Y. Wei, Q. Zheng, and G. Xing, “Public opinion information dissemination in mobile social networks–taking sina weibo as an example,” Information Discovery and Delivery, 2020.
  25. A. Marin and B. Wellman, “Social network analysis: An introduction,” The SAGE handbook of social network analysis, vol. 11, p. 25, 2011.
  26. M. E. Newman, “Modularity and community structure in networks,” Proceedings of the national academy of sciences, vol. 103, no. 23, pp. 8577–8582, 2006.
  27. G. Palla, A.-L. Barabási, and T. Vicsek, “Quantifying social group evolution,” Nature, vol. 446, no. 7136, pp. 664–667, 2007.

Summary

We haven't generated a summary for this paper yet.

Whiteboard

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Collections

Sign up for free to add this paper to one or more collections.