Block Kronecker Product Graph Model
نویسندگان
چکیده
Since Kronecker Product Graph Model (KPGM) was introduced, it has been widely used to model real networks. The characteristics of the model specially its single fractal structure 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 utilizing multiple fractal structures, generating networks with characteristics not generated by previous Kronecker models. We evaluate the block-KPGM by comparing it against two types of Kronecker models. We compare the cumulative distribution functions over three characteristics to show that block-KPGMs are able to produce networks that more closely match real-world graphs, reducing the multidimensional Kolgomorov-Smirnov distance between 11% to 63%.
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