The paper introduces a new measure of betweenness centrality, called *random-walk betweenness*, which relaxes the assumption that information spreads only along shortest paths in a network. This measure is based on the frequency of a node being traversed by random walks between two other nodes, providing a more comprehensive view of a node's influence over information flow. The authors show that this measure can be calculated using matrix methods and provide examples of its application to various networks, including social networks and collaboration networks. They compare the random-walk betweenness with other centrality measures, such as shortest-path betweenness and flow betweenness, highlighting its advantages in capturing nodes that do not lie on geodesic paths but still play significant roles in information dissemination. The paper concludes by discussing the computational efficiency of the method and its potential for further research.The paper introduces a new measure of betweenness centrality, called *random-walk betweenness*, which relaxes the assumption that information spreads only along shortest paths in a network. This measure is based on the frequency of a node being traversed by random walks between two other nodes, providing a more comprehensive view of a node's influence over information flow. The authors show that this measure can be calculated using matrix methods and provide examples of its application to various networks, including social networks and collaboration networks. They compare the random-walk betweenness with other centrality measures, such as shortest-path betweenness and flow betweenness, highlighting its advantages in capturing nodes that do not lie on geodesic paths but still play significant roles in information dissemination. The paper concludes by discussing the computational efficiency of the method and its potential for further research.