Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved towards Low-Entropy Dynamics

Marr, Carsten; Hütt, Marc‐Thorsten

doi:10.3390/e14060993

Cited by 12 publications

(5 citation statements)

References 66 publications

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“…Such cellular automata on graphs are a well established method for probing the relationship between network architecture and dynamics (see, e.g., [20,23,24]). It is clear that discrete models need to be used with a certain care, as some dynamical effects can indeed be artifacts of the (time and state) discretizations.…”

Section: Introductionmentioning

confidence: 99%

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

Fretter

Lesne

Hilgetag

et al. 2017

Sci Rep

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Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.

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Section: Introductionmentioning

confidence: 99%

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

Fretter

Lesne

Hilgetag

et al. 2017

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…The article discusses implications in the context of information transmission in complex network systems. As presented in Reference [76], CA on graphs are a parameter-efficient framework for exploring the relationship between network architecture and dynamics from the perspective of pattern In Reference [77], O'Sullivan presented a new type of dynamic spatial model. The author showed that a graph-CA with specified structural properties that are defined by the relationships between the subsets of cells is a very useful generalisation of the classical CA.…”

Section: Information-spreading Modelling With Cellular Automata and Gmentioning

confidence: 99%

Modelling the Impact of Transit Media on Information Spreading in an Urban Space Using Cellular Automata

2019

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Information spreading processes are the key drivers of marketing campaigns. Activity on social media delivers more detailed information compared to viral marketing in traditional media. Monitoring the performance of outdoor campaigns that are carried out using this transportation system is even more complicated because of the lack of data. The approach that is presented in this paper is based on cellular automata and enables the modelling of the information-spreading processes that are initiated by transit advertising within an urban space. The evaluation of classical and graph cellular automata models and a coverage analysis of transit advertising based on tram lines were performed. The results demonstrated how the number of lines affects the performance in terms of coverage within an urban space and the differences between the proposed models. While research is based on an exemplary dataset taken from Szczecin (Poland), the presented framework can be used together with data from the public transport system for modelling advertising resources usage and coverage within the urban space.

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“…In the last decade, the fusion of networks and cellular automata, appeared into the Literature [17][18][19][20]. This discrete dynamical system, called as Network Automata (NA), uses the network structure as the tessellation of the cellular automaton, whose dynamics is governed by a rule that defines the states of its nodes at each time step.…”

Section: Introductionmentioning

confidence: 99%

Authorship attribution based on Life-Like Network Automata

et al. 2018

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The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.

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Cellular Automata on Graphs: Topological Properties of ER Graphs Evolved towards Low-Entropy Dynamics

Cited by 12 publications

References 66 publications

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs

Modelling the Impact of Transit Media on Information Spreading in an Urban Space Using Cellular Automata

Authorship attribution based on Life-Like Network Automata

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