article · 2016

Block Kronecker Product Graph Model

S. Moreno, Purdue Unversity, P. Robles, Purdue Unversity, J. Neville, Purdue Unversity

Cited by 0Abstract and details only

Abstract

Since Kronecker Product Graph Model (KPGM) was intro-duced, it has been widely used to model real networks. The characteristics of the model specially its single fractal struc-ture have made KPGM one of the most important algorithm of the last years. However, the utilization of a single fractal structure decreases the potential of KPGM by limiting the graph space covered by the model. In this paper, we propose a new generalization of KPGM, called block-KPGM. This new model expands the graph space covered for KPGM uti-lizing multiple fractal structures, generating networks with characteristics not generated by previous Kronecker mod-els. We evaluate the block-KPGM by comparing it against two types of Kronecker models. We compare the cumula-tive distribution functions over three characteristics to show that block-KPGMs are able to produce networks that more closely match real-world graphs, reducing the multidimen-sional Kolgomorov-Smirnov distance between 11 % to 63%.

Cite this paper

APA 7

Moreno, S., Unversity, P., Robles, P., Unversity, P., Neville, J., & Unversity, P. (2016). Block Kronecker Product Graph Model.

MLA 9

Moreno, S., et al. “Block Kronecker Product Graph Model.” 2016.

Chicago

Moreno, S., Purdue Unversity, P. Robles, Purdue Unversity, J. Neville, and Purdue Unversity. 2016. “Block Kronecker Product Graph Model.”

Harvard

Moreno, S. et al. (2016) ‘Block Kronecker Product Graph Model’.

BibTeX

@article{moreno2016block,
  title = {{Block Kronecker Product Graph Model}},
  author = {Moreno, S. and Unversity, Purdue and Robles, P. and Unversity, Purdue and Neville, J. and Unversity, Purdue},
  year = {2016}
}

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