Abstract
Most memes die soon after they have been released, but only few go viral and spread worldwide. Identifying the secret recipe for the success of such viral memes is a very interesting ongoing research question. While many researchers have attributed the success of a meme to its content and place of origin, we propose taking into consideration the underlying network structure that the meme propagates on. In this paper, we induce artificial virality in a meme by intelligently directing its trajectory in the network. This induction is based upon the spreading power of core nodes in a core-periphery structure. This paper puts forward two greedy hill climbing approaches to determine the path from a node in the periphery shell (where the memes generally originate) to the core of the network. We also unearth specialized shells—Pseudo-Core, which emulate the behavior of the core in terms of spreading power. We consider two sets for the target nodes, one being core and the other being any of the pseudo-cores. We show that our algorithms perform better than random and degree based approaches and have a worst case time complexity of O(n). The paper highlights the importance of core-periphery structure in a network and the role of pseudo-cores in making a meme go viral.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
portmanteau word of Tamil and English.
- 2.
The core was defined as a set of nodes densely connected to each other having a large number of connections to the periphery nodes. On the other hand, the periphery nodes although connected to the core nodes are largely disconnected amongst themselves.
- 3.
Most of the networks in real world are scale free [7]. Works done by Della et al. [8] and Liu et al. [6] prove that scale-free networks usually possess a core-periphery structure. Therefore by transition, it becomes evident that a social network is a scale free network as well as a core-periphery structure.
- 4.
This algorithm is explained in detail in [10].
- 5.
Without loss of generality, we have ignored the trivial case where source nodes are directly connected to the core as the path length in these cases is 1.
- 6.
The exact calculation for finding the leakage power of a shell can be found in [10].
References
Jiang, L., Miao, Y., Yang, Y., Lan, Z., Hauptmann, A.G.: Viral video style: a closer look at viral videos on youtube. In: Proceedings of International Conference on Multimedia Retrieval, p. 193. ACM (2014)
H.G.S. went Viral: Gangnam style takes the world by storm
Allocca, K.: Why videos go viral. Ted talk (2011)
Kitsak, M., Gallos, L.K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H.E., Makse, H.A.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)
Borgatti, S.P., Everett, M.G.: Models of core/periphery structures. Soc. Netw. 21(4), 375–395 (2000)
Zafarani, R., Liu, H.: Social computing data repository at ASU (2009). http://socialcomputing.asu.edu
Barabási, A.-L., et al.: Scale-free networks: a decade and beyond. science 325(5939), 412 (2009)
Della Rossa, F., Dercole, F., Piccardi, C.: Profiling core-periphery network structure by random walkers. Scientific reports, vol. 3 (2013)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, Jun 2014. http://snap.stanford.edu/data
Gupta, Y., Das, D., Iyengar, S.: Pseudo-cores: the terminus of an intelligent viral meme’s trajectory (2015). arXiv preprint arXiv:1507.07833
Batagelj, V., Zaversnik, M.: An o (m) algorithm for cores decomposition of networks (2003). arXiv preprint arXiv:cs/0310049
Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)
Noh, J.D., Rieger, H.: Random walks on complex networks. Phys. Rev. Lett. 92(11), 118701 (2004)
Adamic, L.A., Lukose, R.M., Puniyani, A.R., Huberman, B.A.: Search in power-law networks. Phys. Rev. E 64(4), 046135 (2001)
Culotta, A.: Towards detecting influenza epidemics by analyzing twitter messages. In: Proceedings of the First Workshop on Social Media Analytics, pp. 115–122. ACM (2010)
Sang, E.T.K., Bos, J.: Predicting the 2011 dutch senate election results with twitter. In: Proceedings of the Workshop on Semantic Analysis in Social Media. Association for Computational Linguistics, pp. 53–60 (2012)
Gerber, M.S.: Predicting crime using twitter and kernel density estimation. Decis. Support Syst. 61, 115–125 (2014)
Daley, D.J., Kendall, D.G.: Epidemics and rumours (1964)
Berger, J., Milkman, K.L.: What makes online content viral? J. Mark. Res. 49(2), 192–205 (2012)
Weng, L., Menczer, F., Ahn, Y.-Y.: Virality prediction and community structure in social networks. Sci. Rep. 3, 2522 (2013)
Milgram, S.: The small world problem. Psychol. Today 2(1), 60–67 (1967)
Kleinberg, J.: Complex networks and decentralized search algorithms (2006)
Acknowledgments
The authors would like to thank the IIT Ropar HPC committee for providing the resources to perform experiments. S.R.S. Iyengar was partially supported by the ISIRD grant (Ref. No. IITRPR/Acad./359) from IIT Ropar. Further, we express our gratitude to the Indian Academy of Sciences, Bangalore for providing us with partial funding to carry out this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Gupta, Y., Das, D., Iyengar, S.R.S. (2016). Pseudo-Cores: The Terminus of an Intelligent Viral Meme’s Trajectory. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-30569-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30568-4
Online ISBN: 978-3-319-30569-1
eBook Packages: EngineeringEngineering (R0)