Abstract
Many data sets which are studied by Information
Systems researchers involve networks that exhibits community structure. Dividing
the large networks into manageable groups (communities) is a crucial first step
to understand the network in macro scale. Which then enables the researchers to
analyze the data in meso-scale. In our previous work we presented Stochastic
Block Model approach and compared the metadata with the ground truth. In present
study we introduce a statistical technique called neoSBM that can reveal the
relationship between metadata and the community structure on the same
real-world school best friendship data set.
Keywords
References
- Bui, T. N., & Jones, C. (1992). Finding good approximate vertex and edge partitions is NP-hard. Information Processing Letters, 42(3), 153-159
- Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
- Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS quarterly, 1189-1216.
- Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36-41.
- 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.
- Karrer, B., & Newman, M. E. (2011). Stochastic blockmodels and community structure in networks. Physical review E, 83(1), 016107.
- 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).
- Newman, M. E. (2002). Assortative mixing in networks. Physical review letters, 89(20), 208701.
- Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2), 026113.
- Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.
- Peel, L., Larremore, D. B., & Clauset, A. (2017). The ground truth about metadata and community detection in networks. Science advances, 3(5), e1602548.
- Perdahci, Z. N., Aydin, M. N., & Kariniauskaitė, D. (2017). Dynamic Loyal Customer Behavior for Community Formation: A Network Science Perspective.
- Perdahcı, Z. N., Aydın, M. N., & Kafkas, K. (2019). SBM Based Community Detection: School Friendship Network.
- Zhang, K., Bhattacharyya, S., & Ram, S. (2016). Large-Scale Network Analysis for Online Social Brand Advertising. Mis Quarterly, 40(4).
Abstract
Yönetim Bilişim Sistemleri
araştırmacılarının ilgi alanına giren ağların birçoğunda topluluk yapısına
rastlanır. Bu makro ölçekli yapılarda doğal olarak ortaya çıkan toplulukların
tespit edilmesi büyük veri kümelerinin yönetilebilir gruplara ayrılması
açısından gereklidir. Böylece bu sistemlerin orta ölçekte anlaşılabilir hale
gelmesi mümkün olur. Önceki çalışmamızda, Stokastik Blok Modelleme yaklaşımını
kullanarak üst veri ve temel gerçeği karşılaştırdık. Bu çalışmamızda, üst veri
ile topluluk yapısının ilişkisini ölçebilen bir istatistiksel yöntem olan
neoSBM’i bir gerçek dünya arkadaşlık ağı veri seti üzerinde uygulayarak
sunuyoruz.
References
- Bui, T. N., & Jones, C. (1992). Finding good approximate vertex and edge partitions is NP-hard. Information Processing Letters, 42(3), 153-159
- Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
- Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS quarterly, 1189-1216.
- Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36-41.
- 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.
- Karrer, B., & Newman, M. E. (2011). Stochastic blockmodels and community structure in networks. Physical review E, 83(1), 016107.
- 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).
- Newman, M. E. (2002). Assortative mixing in networks. Physical review letters, 89(20), 208701.
- Newman, M. E., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2), 026113.
- Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the national academy of sciences, 103(23), 8577-8582.
- Peel, L., Larremore, D. B., & Clauset, A. (2017). The ground truth about metadata and community detection in networks. Science advances, 3(5), e1602548.
- Perdahci, Z. N., Aydin, M. N., & Kariniauskaitė, D. (2017). Dynamic Loyal Customer Behavior for Community Formation: A Network Science Perspective.
- Perdahcı, Z. N., Aydın, M. N., & Kafkas, K. (2019). SBM Based Community Detection: School Friendship Network.
- Zhang, K., Bhattacharyya, S., & Ram, S. (2016). Large-Scale Network Analysis for Online Social Brand Advertising. Mis Quarterly, 40(4).
Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | June 15, 2020 |
| Published in Issue | Year 2020 Volume: 6 Issue: 1 |