HSTGNN jointly models spatial graph structure and temporal dynamics across pressure, flow, and temperature variables to produce accurate virtual measurements in district heating networks.
Veličković, G
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Content embeddings from SBERT enable AUROC above 0.89 for attack detection in MCP tool-call sessions, with tree ensembles on pooled embeddings reaching 0.975 and outperforming GNNs when using task-stratified splits instead of random ones.
citing papers explorer
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Virtual Smart Metering in District Heating Networks via Heterogeneous Spatial-Temporal Graph Neural Networks
HSTGNN jointly models spatial graph structure and temporal dynamics across pressure, flow, and temperature variables to produce accurate virtual measurements in district heating networks.
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Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocols
Content embeddings from SBERT enable AUROC above 0.89 for attack detection in MCP tool-call sessions, with tree ensembles on pooled embeddings reaching 0.975 and outperforming GNNs when using task-stratified splits instead of random ones.