The increasing application of network models to translate and analysis of biological systems discusses the necessity of novel methodological and informatics insights for dealing with biological complexity.

Today, using tools from graph theory to simulate the dynamical system and to understand the behavior of a biological network system such as tumor growth is unavoidable. Perhaps the most significant level of network analysis comes from mining the network measures and configuration data which allow us to explore deeper in multi-scale and multi-level biological models.

This paper presents a graph agent-based tumor model which allows us to mine network measures and visualizing the evolving behavior of tumor in molecular and cellular levels. Besides, in this paper, we have applied three applicable techniques to discover and classify subgraphs in a growing network of agents for the use of a cluster computing system.

%B International Conference on Modeling and Applied Simulation 2019 (MAS19) %I DIME, Università di Genova %C Lisbon, Portugal %V 1 %P 64-71 %8 09/2019 %@ ISBN 978-88-85741-30-0 (paperback) %U http://www.msc-les.org/conf/mas2019/index.html