Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

Abstract

Accurately tracking the influential nodes in swarm is the key to improve the control or defense against the drone combat. But the identification of influential nodes is intractable due to hardly capturing the pattern information of the running drones based on the traditional Graph Theory. To tackle this issue, in this paper, we propose a novel framework to identify the influential nodes, which includes Latent Interaction Graph Extraction (LIGE), Degree Centrality based Influential Inference (DCII) and Formation Stability Coefficient based Re-ranking (FSCR). LIGE is employed to reconstruct the latent interaction graph from the dynamic trajectory data by exploiting the encoder in Neural Relational Inference. DCII invokes the traditional graph model and measures the significance of nodes. In the re-ranking module FSCR, we introduce a novel metric, the formation stability coefficient, to measure swarm formation stability and re-ranking the candidates. The experimental results on the Drone swarm datasets demonstrate that our framework can effectively identify the influential nodes of drone swarm. The extracting time in LIGE is relatively long, which makes it difficult to recognize the core node within the limit time. By optimizing the encoder structure, the extraction time can be reduced to at least twice the original.