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A Structural Hole Spanner (SHS) is a set of nodes in a network that act as abridge among different otherwise disconnected communities. Numerous solutionshave been proposed to discover SHSs that generally require high run time onlarge-scale networks. Another challenge is discovering SHSs across differenttypes of networks for which the traditional one-model-fit-all approach fails tocapture the inter-graph difference, particularly in the case of diversenetworks. Therefore, there is an urgent need of developing effective solutionsfor discovering SHSs in large-scale and diverse networks. Inspired by therecent advancement of graph neural network approaches on various graphproblems, we propose graph neural network-based models to discover SHS nodes inlarge scale networks and diverse networks. We transform the problem into alearning problem and propose an efficient model GraphSHS, that exploits boththe network structure and node features to discover SHS nodes in large scalenetworks, endeavouring to lessen the computational cost while maintaining highaccuracy. To effectively discover SHSs across diverse networks, we proposeanother model Meta-GraphSHS based on meta-learning that learns generalizableknowledge from diverse training graphs (instead of directly learning the model)and utilizes the learned knowledge to create a customized model to identifySHSs in each new graph. We theoretically show that the depth of the proposedgraph neural network model should be at least $\Omega(\sqrt{n}/\log n)$ toaccurately calculate the SHSs discovery problem. We evaluate the performance ofthe proposed models through extensive experiments on synthetic and real-worlddatasets. Our experimental results show that GraphSHS discovers SHSs with highaccuracy and is at least 167.1 times faster than the comparative methods onlarge-scale real-world datasets.

Effective Graph-Neural-Network based Models for Discovering Structural Hole Spanners in Large-Scale and Diverse Networks