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Generalization of consensus processes 
in dynamic networks 
M. Rebollo, J.C. Losada, J. Galeano, R.M. Benito 
Univ. Politècnica de València, Univ. Politécnica de Madrid 
Ruled by 
converges to 
0.5 
0.45 
0.4 
0.35 
0.3 
0.25 
0.2 
0.15 
0.1 
0 2 4 6 8 10 
time 
x 
Consensus process 
x 
1 
x 
2 
x 
3 
x 
4 
xt+1 
i = xti 
+ " 
X 
j2Nt 
i 
⇥ 
xt 
j  xti 
⇤ 
lim 
t!1 
xi = 
P 
i x0i 
N
key concept: 
sum conservation 
s = 
X 
i 
x0i 
= 
X 
i 
xti 
8t 
values can be 
corrected locally 
t+1 
i = min 
j2Nt 
i [i 
t 
j 
xt+1 
i = xti 
+ f(xti 
) + 
ti 
wt 
i 
X 
j2Nt 
i 
⇥ 
xt 
j  xti 
⇤ 
+ ti 
local corrections adaption to dynamic ℇ
0.5 
0.45 
0.4 
0.35 
0.3 
0.25 
0.2 
0.15 
0.1 
0.4 
0.35 
0.3 
0.25 
0.2 
0.15 
0.1 
0.05 
0 2 4 6 8 10 
time 
x 
Consensus process 
0.5 
0.45 
0.4 
0.35 
0.3 
Consensus with changes in initial values 
x 
0.25 
0.2 
0.15 
0.1 
time 0 5 10 15 
0.5 
0.45 
0.4 
0.35 
0.3 
0.25 
0.2 
0.15 
0.1 
0 2 4 6 8 10 
time 
x 
Consensus process with node deletion 
0 5 10 15 
time 
x 
Consensus with changes in weights 
Adapts to 
changes in 
• initial values 
• weights 
• network structure 
• learning parameter

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Generalized consensus process in dynamic networks

  • 1. Generalization of consensus processes in dynamic networks M. Rebollo, J.C. Losada, J. Galeano, R.M. Benito Univ. Politècnica de València, Univ. Politécnica de Madrid Ruled by converges to 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0 2 4 6 8 10 time x Consensus process x 1 x 2 x 3 x 4 xt+1 i = xti + " X j2Nt i ⇥ xt j xti ⇤ lim t!1 xi = P i x0i N
  • 2. key concept: sum conservation s = X i x0i = X i xti 8t values can be corrected locally t+1 i = min j2Nt i [i t j xt+1 i = xti + f(xti ) + ti wt i X j2Nt i ⇥ xt j xti ⇤ + ti local corrections adaption to dynamic ℇ
  • 3. 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 2 4 6 8 10 time x Consensus process 0.5 0.45 0.4 0.35 0.3 Consensus with changes in initial values x 0.25 0.2 0.15 0.1 time 0 5 10 15 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0 2 4 6 8 10 time x Consensus process with node deletion 0 5 10 15 time x Consensus with changes in weights Adapts to changes in • initial values • weights • network structure • learning parameter