Network modeling has been explored extensively by means of theoretical analysis as well as numerical simulations for Network Reconstruction (NR). The network reconstruction problem requires the estimation of the power-law exponent (
γ
) of a given input network. Thus, the effectiveness of the NR solution depends on the accuracy of the calculation of
γ
. In this article, we re-examine the degree distribution-based estimation of
γ
, which is not very accurate due to approximations. We propose
X
-distribution, which is more accurate as compared to degree distribution. Various state-of-the-art network models, including CPM, NRM, RefOrCite2, BA, CDPAM, and DMS, are considered for simulation purposes, and simulated results support the proposed claim. Further, we apply
X
-distribution over several real-world networks to calculate their power-law exponents, which differ from those calculated using respective degree distributions. It is observed that
X
-distributions exhibit more linearity (straight line) on the log-log scale as compared to degree distributions. Thus,
X
-distribution is more suitable for the evaluation of power-law exponent using linear fitting (on the log-log scale). The MATLAB implementation of power-law exponent (
γ
) calculation using
X
-distribution for different network models, and the real-world datasets used in our experiments are available here: https://github.com/Aikta-Arya/X-distribution-Retraceable-Power-Law-Exponent-of-Complex-Networks.git