graph-structure-learning

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papersTODAY 04:00 UTC

Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection

A new arXiv paper introduces GSLAD, an unsupervised method for detecting anomalies in multivariate time series. The approach focuses on changes in the structural relationships between variables, which often appear before individual readings deviate, and regularizes learned graph structures with prototypes. The authors argue this addresses a gap in forecasting- and reconstruction-based detection methods.

anomaly-detection/time-series/graph-learning/unsupervised

papersSEP 11 04:00 UTC

arXiv Paper Proposes Framework for Learning Time-Varying Graphs from Limited Data

A revised arXiv paper presents a framework for inferring time-varying graphs from spatiotemporal measurements. By assuming a prior on how signals change over time, the method estimates changing graph structures even when only a small number of samples are available. The work targets settings where relationships between nodes shift dynamically rather than staying fixed.

graph-learning/research/arxiv/spatiotemporal