A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
The graph neural network model
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A Graph Koopman Autoencoder enables scalable SDN control for LEO mega-constellations by compressing topology and forecasting dynamics, showing 42.8% better spatial compression and 10.81% better temporal forecasting than baselines in Starlink simulations.
A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.
citing papers explorer
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Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
A graph neural network learns to approximate altruistic robot transfers across heterogeneous teams using Hamilton's rule, achieving near-optimal allocation in simulated firefighting scenarios.
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Toward Scalable SDN for LEO Mega-Constellations: A Graph Learning Approach
A Graph Koopman Autoencoder enables scalable SDN control for LEO mega-constellations by compressing topology and forecasting dynamics, showing 42.8% better spatial compression and 10.81% better temporal forecasting than baselines in Starlink simulations.
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Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder
A masked graph autoencoder on heterogeneous bidirectional graphs predicts per-flow NetFlow attachments and features from sliding windows of network traffic.
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Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.