Ground Truth in Network Communities and Metadata-Aware Community Detection: A Case of School Friendship Network

Abstract

Real-world networks are everywhere and can represent biological, technological, and social interactions. They constitute complicated structures in terms of type of things and their relations. Understanding the network requires better examination of the network structure that can be achieved at various scales including macro, meso, and micro. This research is concerned with meso scale for a student best friendship network where sub-structures in which groups of entities (students) take different functions. In this study we address the following research questions: To what extent would NeoSBM as a stochastic process underlie best friendship interaction and in -turn ground truth interactions (i.e. reported best friendship)? Do metadata such as gender or class contribute to this understanding? How can one support school managers from a meta-data aware community detection perspective? Our findings suggest that metadata aware community detection can be an effective method in supporting decision-making for class formation and group formation for in and out school activities.

Keywords

• SBM
• Community Detection
• neoSBM

References

1. Allaire, J. (2012). RStudio: integrated development environment for R. Boston, MA, 537, 538.
2. Aytac, T. (2015). The relationship between teachers’ perception about school managers’ talent management leadership and the level of organizational commitment. Eurasian Journal of Educational Research, 15(59), 165-180. Bui, T. N., & Jones, C. (1992). Finding good approximate vertex and edge partitions is NP-hard. Information Processing Letters, 42(3), 153-159
3. Barabási, Albert-László (2009) “"Scale-free networks: a decade and beyond.”" science 325, no. 5939 412-413
4. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
5. Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS quarterly, 1189-1216.
6. Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36-41.
7. Golbeck, J., Gerhard, J., O’Colman, F., & O’Colman, R. (2017). Scaling Up Integrated Structural and Content-Based Network Analysis. Information Systems Frontiers, 1-12.
8. Hopkins, M. (2017). A Review of social network analysis and education: Theory, methods, and applications.

9. Karrer, B., & Newman, M. E. (2011). Stochastic block models and community structure in networks. Physical review E, 83(1), 016107., S. M., Kim, I., & Summers, J. D. (2015). Jamming with Social Media: How Cognitive Structuring of Organizing Vision Facets Affects IT Innovation Diffusion. Mis Quarterly, 39(3).
10. Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with Social Media: How Cognitive Structuring of Organizing Vision Facets Affects IT Innovation Diffusion. Mis Quarterly, 39(3).
11. Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2), 026113.
12. Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.
13. Newman, M. E. (2002). Assortative mixing in networks. Physical review letters, 89(20), 208701.
14. Riolo, M. A., & Newman, M. E. J. (2019). Consistency of community structure in complex networks. arXiv preprint arXiv:1908.09867.
15. Peel, L., Larremore, D. B., & Clauset, A. (2017). The ground truth about metadata and community detection in networks. Science advances, 3(5), e1602548.
16. Perdahcı, Z. N., Aydın, M. N., Kafkas, K. (2018, October) SBM Based Community Detection: School Friendship Network. Paper presented at the Fifth International Management Information Systems Conference.
17. Perdahci, Z. N., Aydin, M. N., & Kariniauskaitė, D. (2017). Dynamic Loyal Customer Behavior for Community Formation: A Network Science Perspective.
18. Van Geel, M., Keuning, T., Visscher, A. J., & Fox, J. P. (2016). Assessing the effects of a school-wide data-based decision-making intervention on student achievement growth in primary schools. American Educational Research Journal, 53(2), 360-394.
19. Zhang, K., Bhattacharyya, S., & Ram, S. (2016). Large-Scale Network Analysis for Online Social Brand Advertising. Mis Quarterly, 40(4).