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Identifying the most influential nodes in networked systems is vital to optimize their function and control. Several scalar metrics have been proposed to that effect, but the recent shift in focus towards higher-order networks has rendered them void of performance guarantees. We propose a new measure of nodes centrality, which is no longer a scalar value, but a vector with dimension one lower than the highest order of interaction in the graph. Such a vectorial measure is linked to the eigenvector centrality for networks containing only pairwise interactions, whereas it has a significant added value in all other situations where interactions occur at higher-orders. In particular, it is able to unveil different roles which may be played by a same node at different orders of interactions, an information which is impossible to be retrieved by single scalar measures.
Complex networks represent the natural backbone to study epidemic processes in populations of interacting individuals. Such a modeling framework, however, is naturally limited to pairwise interactions, making it less suitable to properly describe soc
Complex networks are characterized by heterogeneous distributions of the degree of nodes, which produce a large diversification of the roles of the nodes within the network. Several centrality measures have been introduced to rank nodes based on thei
Complex networks or graphs provide a powerful framework to understand importance of individuals and their interactions in real-world complex systems. Several graph theoretical measures have been introduced to access importance of the individual in sy
We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the
Complex systems made of interacting elements are commonly abstracted as networks, in which nodes are associated with dynamic state variables, whose evolution is driven by interactions mediated by the edges. Markov processes have been the prevailing p