LLNA: Lifelike Network Automata
Gisele Helena Barboni Miranda, Jeaneth Machicao, Odemir M. Bruno
The LifeLike Network Automata (LLNA) was designed for pattern recognition in networks. LLNA uses the network topology as the tessellation of a Cellular Automata (CA), whose dynamics produces a spatiotemporal pattern used to extract the attributes for network characterization. The implemented CA is inspired by the rules of LifeLike as illustrated below. LLNA is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability. The reader can found more details at references:
 Miranda, G. H. B., Machicao, J. and Bruno, O. M. Exploring spatiotemporal dynamics of cellular automata for pattern recognition in networks. Scientific Reports 6, 37329 (2016)
 Machicao, J., CorrĂȘa Jr E. A., Miranda, G. H. B., Amancio, D. R. and Bruno, O. M. Authorship attribution based on Lifelike network automata. ArXiv eprints 1610.06498 (2016)
The figure below presents an overview of LLNA.
LLNA
a) Modeling a binary cellular automaton over the network topology. Black cells represent the nodes in the ``on'' state and white cells, the nodes in the ``off'' state. 

b) Spatiotemporal diagram of the evolved automaton. Each column of the diagram represents the evolution of a single node and each row represents the configuration of the states at each time step. 

c) Network descriptor represented by a vector of attributes obtained from the previous diagram. 
LLNA Demo
Datasets
Synthetic dataset
Composed of synthetic networks generated according to the following models: (i) Random; (ii) Smallworld, with rewiring probability of $p=0.1$; (iii) Scalefree, with both linear and nonlinear preferential attachment, and, (iv) Geographical. For each model, there are networks with the following values of mean degree (
Synthetic scalefree dataset Composed of scalefree networks generated according to the models proposed by Barabasi & Albert [1] and Dorogovtsev & Mendes [2]. For the first model, we generated networks with the following values of power law exponent ($\gamma$): 0.5, 1.0, 1.5 and 2.0. Therefore, we have five classes in this dataset, the four different $\gamma$ exponent networks and the networks generated through the second scalefree model. The dataset contains 100 networks for each of these five classes with $N = 1000$ nodes and $\langle k \rangle=8$. Download here
[1] Barabasi, A.L. & Albert, R. Emergence of scaling in random networks. Science 286, 509512 (1999).
[2] Dorogovtsev, S. N. & Mendes, J. F. Evolution of networks. Adv. Phys. 51, 10791187 (2002).