  Structural Hole (SH) theory states that the node which acts as a connecting
link among otherwise disconnected communities gets positional advantages in the
network. These nodes are called Structural Hole Spanners (SHS). SHSs have many
applications, including viral marketing, information dissemination, community
detection, etc. Numerous solutions are proposed to discover SHSs; however, most
of the solutions are only applicable to static networks. Since real-world
networks are dynamic networks; consequently, in this study, we aim to discover
SHSs in dynamic networks. Discovering SHSs is an NP-hard problem, due to which,
instead of discovering exact k SHSs, we adopt a greedy approach to discover
top-k SHSs. Motivated from the success of Graph Neural Networks (GNNs) on
various graph mining problems, we design a Graph Neural Network-based model,
GNN-SHS, to discover SHSs in dynamic networks, aiming to reduce the
computational cost while achieving high accuracy. We analyze the efficiency of
the proposed model through exhaustive experiments, and our results show that
the proposed GNN-SHS model is at least 31.8 times faster and, on an average
671.6 times faster than the comparative method, providing a considerable
efficiency advantage.
