Understanding and controlling spreading dynamics in networks assumes identification of the most influential nodes that will trigger efficient information diffusion. It has been shown that the best spreaders are the ones located in the k-core of the network rather than those with the highest degree or centrality [Kitsak et al., Nature Physics 6, 888–893 (2010)]. In this paper, we further refine the set of the most influential nodes, showing that the nodes belonging to the best K-truss subgraph, as identified by the K-truss decomposition of the network, perform even better. K-truss, being a subset of the k-core of the network, contributes in the reduction of the set of privileged spreaders for information diffusion. We are comparing spreaders belonging to K-truss to those belonging to the rest of the k-core subgraph and those having the highest degrees in the network. Using the SIR epidemic model, we show that such spreaders will influence a greater part of the network during the first steps of the process, but will also cover a larger portion of it at the end of the epidemic – which on average stops at an earlier time step in our case. Furthermore we are studying the robustness of those influential nodes under graph perturbations to examine how they are affected after using various graph noise models. We are finally investigating the impact at the information diffusion associated with multiple initial spreaders which are located in different communities of the networks.