Abstract
The exponential growth in signed social networks in recent years has garnered the interest of numerous researchers in the field. Social balance theory and status theory are the two most prevalent theories of signed social networks and are used for the same purpose. Many researchers have incorporated the concept of social balance theory into their work with community detection problems in order to gain a better understanding of these networks. Social balance theory is suitable for undirected signed social networks; however, it does not consider the direction of the ties formed among users. When dealing with directed signed social networks, researchers simply ignore the direction of ties, which diminishes the significance of the tie direction information. To overcome this, in this chapter we present a mathematical formulation for computing the social status of nodes based on status theory, termed the status factor, which is well suited for directed signed social networks. The status factor is used to quantify social status for each node of overlapping communities in a directed signed social network, and the feasibility of the proposed algorithm for this metric is well illustrated through an example.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agarwal, V., and K.K. Bharadwaj. 2015. Predicting the dynamics of social circles in ego networks using pattern analysis and GA K-means clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5 (3): 113–141.
Anchuri, P., and M. Magdon-Ismail. 2012. Communities and balance in signed networks: A spectral approach. In Advances in social networks analysis and mining, 235–242.
Awal, G.K., and K.K. Bharadwaj. 2017. Leveraging collective intelligence for behavioral prediction in signed social networks through evolutionary approach. Information Systems Frontiers, 1–23.
Facchetti, G., G. Iacono, and C. Altafini. 2011. Computing global structural balance in large-scale signed social networks. Proceedings of the National Academy of Sciences 108 (52): 20953–20958.
Girdhar, N., and K.K. Bharadwaj. 2016. Signed social networks: A survey. In Proceedings of the international conferences on advances in computing and data sciences, 326–335.
Heider, F. 1946. Attitudes and cognitive organization. The Journal of Psychology 21 (1): 107–112.
Kunegis, J., S. Schmidt, A. Lommatzsch, J. Lerner, E.W. De Luca, and S. Albayrak. 2010. Spectral analysis of signed graphs for clustering, prediction and visualization. In Proceedings of the international conferences of SIAM on data mining, 559–570.
Leskovec, J., D. Huttenlocher, and J. Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI conferences on human factors in computing systems, 1361–1370.
Truzzi, M. 1973. An empirical examination of attitude consistency in complex cognitive structures. Systems Research and Behavioral Science 18 (1): 52–59.
Yap, J., and N. Harrigan. 2015. Why does everybody hate me? Balance, status, and homophily: The triumvirate of signed tie formation. Social Networks 40: 103–122.
Zhao, Y., S. Li, and F. Jin. 2016. Identification of influential nodes in social networks with community structure based on label propagation. Neurocomputing 210: 34–44.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Girdhar, N., Bharadwaj, K.K. (2019). Social Status Computation for Nodes of Overlapping Communities in Directed Signed Social Networks. In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-8797-4_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8796-7
Online ISBN: 978-981-10-8797-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)