Dr. Christian M. Meyer

Variation of motifs

in complex networks and its impact on the information processing capacity

Abstract. This thesis discusses the impact of network motifs on the in­for­ma­tion processing capacity within graphs. In directed graphs motifs of size 3 are studied and varied using two presented algorithms. Binary cellular automata are applied on the graphs to analyse the dynamics of the network. The in­for­ma­tion processing capacity is measured by shannon and word entropy, which are determined after every step of variation and visualized in a scatter diagram. A trajectory emerges, which represents the changes in the in­for­ma­tion processing capacity.

Studying a direct correlation of network motifs and the in­for­ma­tion processing capacity is the objective of the thesis. Some motif classes show the ability of establishing greater entropies than others. The motif „feed-forward loop“ was analyzed particularly in this context. Moreover it turned out, that the used transition function as well as the presence of cycles in the graphs has a big impact on the entropies.

Eingereicht: 10.08.2006

Scatter plot of the information processing capacity of a scale-free network whose network motifs are varied.

Scatter plot of the information processing capacity of a scale-free network whose network motifs are varied.