3 research outputs found
From neurons to epidemics: How trophic coherence affects spreading processes
Trophic coherence, a measure of the extent to which the nodes of a directed
network are organised in levels, has recently been shown to be closely related
to many structural and dynamical aspects of complex systems, including graph
eigenspectra, the prevalence or absence of feed-back cycles, and linear
stability. Furthermore, non-trivial trophic structures have been observed in
networks of neurons, species, genes, metabolites, cellular signalling,
concatenated words, P2P users, and world trade. Here we consider two simple yet
apparently quite different dynamical models -- one a
Susceptible-Infected-Susceptible (SIS) epidemic model adapted to include
complex contagion, the other an Amari-Hopfield neural network -- and show that
in both cases the related spreading processes are modulated in similar ways by
the trophic coherence of the underlying networks. To do this, we propose a
network assembly model which can generate structures with tunable trophic
coherence, limiting in either perfectly stratified networks or random graphs.
We find that trophic coherence can exert a qualitative change in spreading
behaviour, determining whether a pulse of activity will percolate through the
entire network or remain confined to a subset of nodes, and whether such
activity will quickly die out or endure indefinitely. These results could be
important for our understanding of phenomena such as epidemics, rumours, shocks
to ecosystems, neuronal avalanches, and many other spreading processes
Collective decision-making on triadic graphs
Many real-world networks exhibit community structures and non-trivial 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