18 research outputs found
Sticky Seeding in Discrete-Time Reversible-Threshold Networks
When nodes can repeatedly update their behavior (as in agent-based models
from computational social science or repeated-game play settings) the problem
of optimal network seeding becomes very complex. For a popular
spreading-phenomena model of binary-behavior updating based on thresholds of
adoption among neighbors, we consider several planning problems in the design
of \textit{Sticky Interventions}: when adoption decisions are reversible, the
planner aims to find a Seed Set where temporary intervention leads to long-term
behavior change. We prove that completely converting a network at minimum cost
is -hard to approximate and that maximizing conversion
subject to a budget is -hard to approximate. Optimization
heuristics which rely on many objective function evaluations may still be
practical, particularly in relatively-sparse networks: we prove that the
long-term impact of a Seed Set can be evaluated in operations. For a
more descriptive model variant in which some neighbors may be more influential
than others, we show that under integer edge weights from
objective function evaluation requires only operations. These
operation bounds are based on improvements we give for bounds on
time-steps-to-convergence under discrete-time reversible-threshold updates in
networks.Comment: 19 pages, 2 figure
Linear And Nonlinear Arabesques: A Study Of Closed Chains Of Negative 2-Element Circuits
In this paper we consider a family of dynamical systems that we call
"arabesques", defined as closed chains of 2-element negative circuits. An
-dimensional arabesque system has 2-element circuits, but in addition,
it displays by construction, two -element circuits which are both positive
vs one positive and one negative, depending on the parity (even or odd) of the
dimension . In view of the absence of diagonal terms in their Jacobian
matrices, all these dynamical systems are conservative and consequently, they
can not possess any attractor. First, we analyze a linear variant of them which
we call "arabesque 0" or for short "A0". For increasing dimensions, the
trajectories are increasingly complex open tori. Next, we inserted a single
cubic nonlinearity that does not affect the signs of its circuits (that we call
"arabesque 1" or for short "A1"). These systems have three steady states,
whatever the dimension is, in agreement with the order of the nonlinearity. All
three are unstable, as there can not be any attractor in their state-space. The
3D variant (that we call for short "A1\_3D") has been analyzed in some detail
and found to display a complex mixed set of quasi-periodic and chaotic
trajectories. Inserting cubic nonlinearities (one per equation) in the same
way as above, we generate systems "A2\_D". A2\_3D behaves essentially as
A1\_3D, in agreement with the fact that the signs of the circuits remain
identical. A2\_4D, as well as other arabesque systems with even dimension, has
two positive -circuits and nine steady states. Finally, we investigate and
compare the complex dynamics of this family of systems in terms of their
symmetries.Comment: 22 pages, 12 figures, accepted for publication at Int. J. Bif. Chao
Words fixing the kernel network and maximum independent sets in graphs
The simple greedy algorithm to find a maximal independent set of a graph can
be viewed as a sequential update of a Boolean network, where the update
function at each vertex is the conjunction of all the negated variables in its
neighbourhood. In general, the convergence of the so-called kernel network is
complex. A word (sequence of vertices) fixes the kernel network if applying the
updates sequentially according to that word. We prove that determining whether
a word fixes the kernel network is coNP-complete. We also consider the
so-called permis, which are permutation words that fix the kernel network. We
exhibit large classes of graphs that have a permis, but we also construct many
graphs without a permis
Sticky Seeding in Discrete-Time Reversible-Threshold Networks
When nodes can repeatedly update their behavior (as in agent-based models from computational social science or repeated-game play settings) the problem of optimal network seeding becomes very complex. For a popular spreading-phenomena model of binary-behavior updating based on thresholds of adoption among neighbors, we consider several planning problems in the design of Sticky Interventions: when adoption decisions are reversible, the planner aims to find a Seed Set where temporary intervention leads to long-term behavior change. We prove that completely converting a network at minimum cost is Ω(ln(OP T ))-hard to approximate and that maximizing conversion subject to a budget is (1 − 1 )-hard to approximate. Optimization heuristics which rely on many objective e-function evaluations may still be practical, particularly in relatively-sparse networks: we prove that the long-term impact of a Seed Set can be evaluated in O(|E|2) operations. For a more descriptive model variant in which some neighbors may be more influential than others, we show that under integer edge weights from {0, 1, 2, ..., k} objective function evaluation requires only O(k|E|2) operations. These operation bounds are based on improvements we give for bounds on time-steps-to-convergence under discrete-time reversible-threshold updates in networks