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arXiv Machine Learning By Md Abrar Jahin, Taufikur Rahman Fuad, Md Rizwan Parvez

Differentiable Koopman Operator for Contrastive Learning on Dynamic Graphs

Read the original on arXiv Machine Learning →

The paper introduces KAIROS, a self‑supervised framework that embeds a differentiable Koopman operator into a dynamic graph contrastive learning loop. By linearizing temporal evolution in the embedding space, KAIROS jointly optimizes raw node features and a graph‑diffused structural view using multi‑granularity contrastive objectives across time windows. For anomaly detection, it combines Koopman prediction residuals with temporal inconsistency and local neighborhood deviation, achieving state‑of‑the‑art results on nine dynamic graph benchmarks and competitive performance on unsupervised node classification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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