If you use a correlation test to generate your co-expression network, the test statistics and thereby a p-value will depend on the degrees of freedom, and thereby be corrected for sample size, such that the same correlation of two larger vectors is more significant than that of a shorter one (resembling your intuition). You might then -in addition- want to correct the p-values for multiple testing.
See for example cor.test in R:
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.test.html
Example:
> x = c(1,1.1,1) ; y = c(1,1.5,1.2)
> cor.test (c(x),c(y))
Pearson's product-moment correlation
data: c(x) and c(y)
t = 2.3094, df = 1, p-value = 0.2601
alternative hypothesis: true correlation is not equal to 0
sample estimates:
cor
0.9176629
> cor.test (c(x,x),c(y,y))
Pearson's product-moment correlation
data: c(x, x) and c(y, y)
t = 4.6188, df = 4, p-value = 0.00989
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.4156606 0.9911073
sample estimates:
cor
0.9176629