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Abstract
Many real-world networks exhibit community structures and nontrivial clustering associated with the occurrence of a considerable number of triangular 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 loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as 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 contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior.
Keywords
PHASE-TRANSITION, NETWORK MOTIFS, NOISE, Complex networks, Triadic motifs, Collective decision-making, Group coherence, Feedforward loop, Hierarchality

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MLA
Rausch, Ilja, et al. “Collective Decision-Making on Triadic Graphs.” Complex Networks XI : Proceedings of the 11th Conference on Complex Networks CompleNet 2020, edited by H Barbosa et al., Springer, 2020, pp. 119–30, doi:10.1007/978-3-030-40943-2_11.
APA
Rausch, I., Khaluf, Y., & Simoens, P. (2020). Collective decision-making on triadic graphs. In H. Barbosa, J. Gomez-Gardenes, B. Gonçalves, G. Mangioni, R. Meneze, & M. Oliveira (Eds.), Complex networks XI : proceedings of the 11th Conference on Complex Networks CompleNet 2020 (pp. 119–130). Cham: Springer. https://doi.org/10.1007/978-3-030-40943-2_11
Chicago author-date
Rausch, Ilja, Yara Khaluf, and Pieter Simoens. 2020. “Collective Decision-Making on Triadic Graphs.” In Complex Networks XI : Proceedings of the 11th Conference on Complex Networks CompleNet 2020, edited by H Barbosa, J Gomez-Gardenes, B Gonçalves, G Mangioni, R Meneze, and M Oliveira, 119–30. Cham: Springer. https://doi.org/10.1007/978-3-030-40943-2_11.
Chicago author-date (all authors)
Rausch, Ilja, Yara Khaluf, and Pieter Simoens. 2020. “Collective Decision-Making on Triadic Graphs.” In Complex Networks XI : Proceedings of the 11th Conference on Complex Networks CompleNet 2020, ed by. H Barbosa, J Gomez-Gardenes, B Gonçalves, G Mangioni, R Meneze, and M Oliveira, 119–130. Cham: Springer. doi:10.1007/978-3-030-40943-2_11.
Vancouver
1.
Rausch I, Khaluf Y, Simoens P. Collective decision-making on triadic graphs. In: Barbosa H, Gomez-Gardenes J, Gonçalves B, Mangioni G, Meneze R, Oliveira M, editors. Complex networks XI : proceedings of the 11th Conference on Complex Networks CompleNet 2020. Cham: Springer; 2020. p. 119–30.
IEEE
[1]
I. Rausch, Y. Khaluf, and P. Simoens, “Collective decision-making on triadic graphs,” in Complex networks XI : proceedings of the 11th Conference on Complex Networks CompleNet 2020, Univ Exeter, ENGLAND, 2020, pp. 119–130.
@inproceedings{8654315,
  abstract     = {{Many real-world networks exhibit community structures and nontrivial clustering associated with the occurrence of a considerable number of triangular 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 loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as 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 contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior.}},
  author       = {{Rausch, Ilja and Khaluf, Yara and Simoens, Pieter}},
  booktitle    = {{Complex networks XI : proceedings of the 11th Conference on Complex Networks CompleNet 2020}},
  editor       = {{Barbosa, H and Gomez-Gardenes, J and Gonçalves, B and Mangioni, G and Meneze, R and Oliveira, M}},
  isbn         = {{9783030409425}},
  issn         = {{2213-8684}},
  keywords     = {{PHASE-TRANSITION,NETWORK MOTIFS,NOISE,Complex networks,Triadic motifs,Collective decision-making,Group coherence,Feedforward loop,Hierarchality}},
  language     = {{eng}},
  location     = {{Univ Exeter, ENGLAND}},
  pages        = {{119--130}},
  publisher    = {{Springer}},
  title        = {{Collective decision-making on triadic graphs}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-40943-2_11}},
  year         = {{2020}},
}

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