  Recently, we have shown that if the $i$th node of the Barab\'{a}si-Albert
(BA) network is characterized by the generalized degree
$q_i(t)=k_i(t)t_i^\beta/m$, where $k_i(t)\sim t^\beta$ and $m$ are its degree
at current time $t$ and at birth time $t_i$, then the corresponding
distribution function $F(q,t)$ exhibits dynamic scaling. Applying the same idea
to our recently proposed mediation-driven attachment (MDA) network, we find
that it too exhibits dynamic scaling but, unlike the BA model, the exponent
$\beta$ of the MDA model assumes a spectrum of value $1/2\leq \beta \leq 1$.
Moreover, we find that the scaling curves for small $m$ are significantly
different from those of the larger $m$ and the same is true for the BA networks
albeit in a lesser extent. We use the idea of the distribution of inverse
harmonic mean (IHM) of the neighbours of each node and show that the number of
data points that follow the power-law degree distribution increases as the
skewness of the IHM distribution decreases. Finally, we show that both MDA and
BA models become almost identical for large $m$.
