A Study on Generation of Synthetic Evolving Social Graph

Nagehan Ilhan, Şule Gündüz Öğüdücü

Abstract

Social networks are popular tools for communication, interaction, and information sharing on the Internet. The extreme popularity and rapid growth of these online social networks reveal to study, understand, and discover their properties. Social networks evolve gradually and the network structure varies as the network grows. Large-scale dynamic network analysis requires a large quantity of network data to be available for the experiments and using real data have restrictions due to the privacy issues. Synthetic data generation is an alternative way to overcome these problems. The challenge when generating synthetic data is having characteristics that are similar to real-world data. In this paper, we study on generating synthetic, but realistic, time-evolving social graphs. We describe two main classes of properties: static and dynamic. We analyzed real datasets and extracted their behavior using static and dynamic properties. Then, we generated synthetic graphs with different parameter settings using Barabasi-Albert model (Barabasi and Albert, 1999). Our work enables the creation of synthetic networks that reflect both static and dynamic characteristics of online social networks. Moreover, our generated data may lead to more accurate structural and growth models, which are useful for network analysis and planning.

References

  1. Barabasi, A.-L. and Albert, R. (1999). Emergence of scaling in random networks.
  2. Chakrabarti, D. and Faloutsos, C. (2006). Graph mining: Laws, generators, and algorithms. ACM Comput. Surv., 38(1).
  3. Erdös, P. and Rényi, A. (1959). On random graphs i. Publicationes Mathematicae Debrecen, 6:290.
  4. Leskovec, J., Chakrabarti, D., Kleinberg, J. M., and Faloutsos, C. (2005a). Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. In Jorge, A., Torgo, L., Brazdil, P., Camacho, R., and Gama, J., editors, PKDD, volume 3721 of Lecture Notes in Computer Science, pages 133-145. Springer.
  5. Leskovec, J. and Faloutsos, C. (2007). Scalable modeling of real graphs using kronecker multiplication. In Proceedings of the 24th international conference on Machine learning, ICML 7807, pages 497-504, New York, NY, USA. ACM.
  6. Leskovec, J., Kleinberg, J., and Faloutsos, C. (2005b). Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, KDD 7805, pages 177-187, New York, NY, USA. ACM.
  7. Mislove, A. (2009). Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems. PhD thesis, Rice University, Department of Computer Science.
  8. Mislove, A., Koppula, H. S., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. (2008). Growth of the flickr social network. In Proceedings of the 1st ACM SIGCOMM Workshop on Social Networks (WOSN'08).
  9. Travers, J. and Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32(4):425- 443.
  10. Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684):440- 442.
Download


Paper Citation


in Harvard Style

Ilhan N. and Gündüz Öğüdücü Ş. (2013). A Study on Generation of Synthetic Evolving Social Graph . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 523-526. DOI: 10.5220/0004258005230526


in Bibtex Style

@conference{icaart13,
author={Nagehan Ilhan and Şule Gündüz Öğüdücü},
title={A Study on Generation of Synthetic Evolving Social Graph},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={523-526},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004258005230526},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Study on Generation of Synthetic Evolving Social Graph
SN - 978-989-8565-39-6
AU - Ilhan N.
AU - Gündüz Öğüdücü Ş.
PY - 2013
SP - 523
EP - 526
DO - 10.5220/0004258005230526