Over the last years, there is a deep interest in the analysis of different communities and complex networks. The problem of identification of key elements and the most important connections in such networks is one of the main areas of research. However, the growth of sizes of real networks as well as their heterogeneity makes the problem both important and problematic. The application of short-range and long-range interaction centrality (SRIC and LRIC correspondingly) indices can be used to solve the problem since they take into account different important aspects such as individual properties of nodes, the possibility of their group influence and topological structure of the whole network. However, the computational complexity of such indices needs a further consideration. Thus, our main focus is on the performance of SRIC and LRIC indices. As a result, we proposed several modes how to decrease their computational complexity of the indices. The runtime comparison of the sequential and parallel computation of the proposed models is also given.