We propose new methods for influence estimation of nodes in a network. Our methodology takes into consideration the intensity of the connections between nodes as well as their individual attributes. We consider different decisive groups of nodes, estimate their influence to each node of the network and then aggregate the results to a single value. A distinct feature of our methods is that they consider both direct and indirect connections between nodes and take into account possible chain reactions in the system. The approach is based on two different ideas: the first one is the analysis of the distance between nodes where we examine the influence through all paths between them; the second one is the analysis of the influence with the help of simulations where we actuate some groups of nodes and then analyze the changes in the system. We impose additional parameters (the maximum path’s length, the size of nodes groups, etc.), which can be changed with respect to the problem and used to reduce the computational complexity. Various methods on how to evaluate the power of each path between nodes and aggregate path’s influences are proposed. We applied our methodology to real networks and compared results with the existing techniques of influence estimation. The results showed that our approach elucidates hidden participants that are influential in the system.