  In this paper, we study the cluster head detection problem of a two-level
unmanned aerial vehicle (UAV) swarm network (USNET) with multiple UAV clusters,
where the inherent follow strategy (IFS) of low-level follower UAVs (FUAVs)
with respect to high-level cluster head UAVs (HUAVs) is unknown. We first
propose a graph attention self-supervised learning algorithm (GASSL) to detect
the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same
time. Then, to detect the HUAVs in the USNET with multiple UAV clusters, we
develop a multi-cluster graph attention self-supervised learning algorithm
(MC-GASSL) based on the GASSL. The MC-GASSL clusters the USNET with a gated
recurrent unit (GRU)-based metric learning scheme and finds the HUAVs in each
cluster with GASSL. Numerical results show that the GASSL can detect the HUAVs
in single UAV clusters obeying various kinds of IFSs with over 98% average
accuracy. The simulation results also show that the clustering purity of the
USNET with MC-GASSL exceeds that with traditional clustering algorithms by at
least 10% average. Furthermore, the MC-GASSL can efficiently detect all the
HUAVs in USNETs with various IFSs and cluster numbers with low detection
redundancies.
