Titlebook: Complex Networks XI; Proceedings of the 1 Hugo Barbosa,Jesus Gomez-Gardenes,Marcos Oliveira Conference proceedings 2020 The Editor(s) (if a

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The Upper Triangular Technology, subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more often compared to random topologies. Two prominent examples, the feedforward loop and the feedback l

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https://doi.org/10.1007/978-3-030-40943-2Computer Science; Informatics; Conference Proceedings; Research; Applications; Graph Theory; Complex Syste

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978-3-030-40945-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Complex Networks XI978-3-030-40943-2Series ISSN 2213-8684 Series E-ISSN 2213-8692

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Unsupervised Strategies to Network Topology Reconfiguration Optimization with Limited Link Addition one with the smallest closeness, although this procedure is not scalable. Furthermore, the strategy that links nodes whose betweenness values are near to the median was shown to be scalable and easy to implement, even if it has not delivered the best performance. We have found that the min-cut proc

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Collective Decision-Making on Triadic Graphsas underlying topologies of systems with collective dynamics inspired from locust marching bands. We demonstrate that the motif type constituting the networks can have a paramount influence on group decision-making that cannot be explained solely in terms of the degree distribution. We find that, in

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Stable Fixation of the Hand and Wrist one with the smallest closeness, although this procedure is not scalable. Furthermore, the strategy that links nodes whose betweenness values are near to the median was shown to be scalable and easy to implement, even if it has not delivered the best performance. We have found that the min-cut proc

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Finite-Dimensional Approximations,edundant and heavy computational steps using combinatorics. We were are able to achieve substantial improvements, with evidence of exponential speedup for our best cases, where these patterns represent up to 97% of the network, from a broad set of real and synthetic networks.