Neural extraction of multiscale essential structure for network dismantling.

Abstract

Diverse real world systems can be abstracted as complex networks consisting of nodes and edges as functional components. Percolation theory has shown that the failure of a few of nodes could lead to the collapse of a whole network, which brings up the network dismantling problem: How to select the least number of nodes to decompose a network into disconnected components each smaller than a predefined threshold? For its NP-hardness, many heuristic approaches have been proposed to measure and rank each node according to its importance to network structural stability. However, these measures are from a uniscale viewpoint by regarding one complex network as a flatted topology. In this article, we argue that nodes' structural importance can be measured in different scales of network topologies. Built upon recent deep learning techniques, we propose a self-supervised learning based network dismantling framework (NEES), which can hierarchically merge some compact substructures to convert a network into a coarser one with fewer nodes and edges. During the merging process, we design neural models to extract essential structures and utilize self-attention mechanisms to learn nodes' importance hierarchy in each scale. Experiments on real world networks and synthetic model networks show that the proposed NEES outperforms the state-of-the-art schemes in most cases in terms of removing the least number of target nodes to dismantle a network. The dismantling effectiveness of our neural extraction framework also highlights the emerging role of multi-scale essential structures.