We propose a simple preferential attachment model of growing network using the complementary probability of Barabási-Albert (BA) model, i.e., $Π(k_i) \propto 1-\frac{k_i}{\sum_j k_j}$. In this network, new nodes are preferentially attached to not well connected nodes. Numerical simulations, in perfect agreement with the master equation solution, give an exponential degree distribution. This suggests that the power law degree distribution is a consequence of preferential attachment probability together with "rich get richer" phenomena. We also calculate the average degree of a target node at time t $(<k_s(t)>)$ and its fluctuations, to have a better view of the microscopic evolution of the network, and we also compare the results with BA model.