Core-GAE: Toward Generation of IoT Networks

To realize simulation experiments in large-scale Internet of Things (IoT) networks, this work studies the utilization of deep graph generative models to generate IoT networks, which can provide an economic approach facilitating IoT to meet the requirements of real-time performance, interoperability, energy efficiency, and coexistence. In IoT, nodes have different attributes, different connection ways with surrounding nodes, and different compactness of the region, which pose great challenges for network generation. By leveraging the properties of k-core and variational autoencoder during network generation, we propose a variable graph autoencoder called Core-GAE incorporating the coreness of nodes. In contrast to previous graph generative models, Core-GAE can preserve the local proximity similarity and maintain the global structural features simultaneously when learning the structural features of graphs. All three of the tasks we experimented with on four data sets show that Core-GAE exhibits better performance than previous ones.

Automated summary

This study investigates the use of deep graph generative models in simulating large-scale Internet of Things (IoT) networks and presents a variable graph autoencoder called Core-GAE that considers the coreness of nodes during network generation. Core-GAE preserves both local proximity similarity and global structural features when learning the structural features of graphs.