COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
Microrank: End-to-end latency issue localization with extended spectrum analysis in microservice environments
2 Pith papers cite this work. Polarity classification is still indexing.
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A two-layer GCN on 21,438 request-level invocation graphs from a Docker-based microservice benchmark reaches 96.2% accuracy under random split but is outperformed by non-graph baselines under stricter trial-level splits.
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COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
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Can Graph-Based Microservice Performance Detection Be Used for Microservice Intrusion Detection?
A two-layer GCN on 21,438 request-level invocation graphs from a Docker-based microservice benchmark reaches 96.2% accuracy under random split but is outperformed by non-graph baselines under stricter trial-level splits.