International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 9, No. 1, 8 Jan. 2019
Cover page and Table of Contents: PDF (size: 777KB)
An Approach to Represent Social Graph as Multi-Layer Graph Using Graph Mining Techniques
Full Text (PDF, 777KB), PP.20-36
Views: 0 Downloads: 0
Author(s)
Index Terms
Adjacency Matrix, Base-Layer, Multi-Layer Graph, Social Graph, Sub-Layer
Abstract
In Social Graph, a set of entities or nodes or vertices interact with each other in a complicated manner that can form multiple types of relationships that depend on time and types of complications. Such graphs include multiple subsystems and layers of connectivity. So it is important to take such multi-layer features into account to make easier of understanding of such complex systems. In this paper, the author focuses on a Social Graph to represent in a multi-layer graph based on its characteristics lies in each node or vertex or entity. For this, the author proposes a general model related to Social Graph. For this model, the author proposes an algorithm, SoGraM for representation of Social Graph with multi-layer features using Graph Mining Techniques. Further, the author tries to prove the proposed algorithm with three examples of Social Graph namely Author Graph, Email Graph, and Telephone Graph.
Cite This Paper
Bapuji Rao,"An Approach to Represent Social Graph as Multi-Layer Graph Using Graph Mining Techniques", International Journal of Education and Management Engineering(IJEME), Vol.9, No.1, pp.20-36, 2019. DOI: 10.5815/ijeme.2019.01.03
Reference
[1] Acar, E. & Yener, B. Unsupervised multiway data analysis: a literature survey. IEEE Trans.Knowl. Data Eng., 21, 6–20, 2009.
[2] Cai, D., Shao, Z., He, X., Yan, X., & Han, J. Community mining from multi-relational networks. Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, 2005.
[3] Dunlavy, D. M., Kolda, T. G., & Kegelmeyer, W. P. Multilinear algebra for analyzing data with multiple linkages. Graph Algorithms in the Language of Linear Algebra (J. Kepner & J. Gilbert eds). Fundamentals of Algorithms. Philadelphia: SIAM, pp. 85–114, 2011.
[4] Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, James P., Moreno, Y., & Porter, Mason A. Multilayer networks. J. Complex Netw., 2, 203–271, 2014.
[5] Kolda, T. G., & Bader, B. W. Tensor decompositions and applications. SIAM Rev., 51, 455–500, 2009.
[6] Krackhardt, D. Cognitive social structures. Soc. Netw., 9, 109–134, 1987.
[7] Oselio, B., Kulesza, A., & Hero, A.O. Multi-layer graph analysis for social networks. University of Michigan, Ann Arbor, MI 48109, USA. Available as arXiv:1309.5124v2, 2014.
[8] Oselio, B., Kulesza, A., & Alfred Hero., A. O. Information extraction from large multi-layer social networks. Proceedings of IEEE Intl Conf on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane. Available as arxiv 1507.00087, 2015.
[9] Rao, B., Mitra, A., & Narayana, U. An approach to study properties and behavior of Social Network using Graph Mining Techniques. In the Proceedings of DIGNATE 2014: ETEECT 2014, 1–6, India, 2014.
[10] Rao, B., Mishra, S. N., & Maharana, H. S. Algorithm for Representation of Call-Duration Graphs of Telephone Graph Using Multi-layer Graph Techniques. Published in Advances in Intelligent Systems and Computing, pp. 415-425, Springer Verlag, DOI 10.1007/978-981-10-2525-9, 2017.
[11] Socievole, A., Yoneki, E., Rango, F. De., & Crowcroft, J. ML-SOR: Message routing using multi-layer social networks in opportunistic communications. Elsevier Computer Networks. http://dx.doi.org/10.1016/j.comnet.2015.02.016, 2015.
[12] Sun, Y. & Han, J. Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Explor. Newslett., 14, 20–28, 2013.
[13] Wasserman, S. & Faust, K. Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press, 1994.
[14] Zhou, D., Orshanskiy, S. A., Zha, H. & Giles, C. L. Co-ranking authors and documents in a heterogeneous network. Proceedings of Seventh IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, pp. 739–744, 2007.