MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks

• URL: http://arxiv.org/abs/2505.09331v1
• Date: Wed, 14 May 2025 12:26:46 GMT
• Title: MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks
• Authors: Cunlai Pu, Fangrui Wu, Rajput Ramiz Sharafat, Guangzhao Dai, Xiangbo Shu,
• Abstract summary: Link prediction in unmanned aerial vehicle (UAV) ad hoc networks (UANETs) aims to predict the potential formation of future links between UAVs.<n>In adversarial environments where the route information of UAVs is unavailable, predicting future links must rely solely on the observed historical topological information of UANETs.<n>We propose a multi-scale structural-temporal link prediction model (MUST) for UANETs.
• Score: 14.111475464877563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Link prediction in unmanned aerial vehicle (UAV) ad hoc networks (UANETs) aims to predict the potential formation of future links between UAVs. In adversarial environments where the route information of UAVs is unavailable, predicting future links must rely solely on the observed historical topological information of UANETs. However, the highly dynamic and sparse nature of UANET topologies presents substantial challenges in effectively capturing meaningful structural and temporal patterns for accurate link prediction. Most existing link prediction methods focus on temporal dynamics at a single structural scale while neglecting the effects of sparsity, resulting in insufficient information capture and limited applicability to UANETs. In this paper, we propose a multi-scale structural-temporal link prediction model (MUST) for UANETs. Specifically, we first employ graph attention networks (GATs) to capture structural features at multiple levels, including the individual UAV level, the UAV community level, and the overall network level. Then, we use long short-term memory (LSTM) networks to learn the temporal dynamics of these multi-scale structural features. Additionally, we address the impact of sparsity by introducing a sophisticated loss function during model optimization. We validate the performance of MUST using several UANET datasets generated through simulations. Extensive experimental results demonstrate that MUST achieves state-of-the-art link prediction performance in highly dynamic and sparse UANETs.

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