Considerable effort has been devoted to the study of network structures and connectivity
patterns and their influence on network dynamics. A widely used assumption in network
analysis models is that traffic follows the shortest paths connecting pairs of nonneighboring
vertices. For example, graph centrality measures, community extraction algorithms,
and core-periphery detection algorithms use this assumption. However, this is a very
restricted perspective and can be misleading as a consequence of its focus on shortest
path communications. In this work, we study the utilization of shortest paths in complex
networks in different data dissemination scenarios. We also explore whether there
are general properties that can make networks utilize shortest paths more effectively.
By conducting simulations on a set of real-world and artificial networks, we show
that the utilization of shortest paths in complex networks may not be as common as
assumed. This implies that longer paths can be as important (in some cases) as the
shortest paths. Our results show that at least two factors clearly influence shortest
path utilization in a network: the structure of the network and the data dissemination
algorithm. We also find that the type of a network is not a good indicator of its
shortest path utilization.
