pyod and pygod
These are complements: PyOD provides general-purpose outlier detection algorithms (tabular, time-series, neural network-based) while PyGOD specializes in detecting anomalies specifically within graph-structured data, so they address different data modalities and can be used together in pipelines that process both relational and graph-based features.
About pyod
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
This helps data analysts and researchers identify unusual or suspicious data points within large, complex datasets. You input your tabular data, and it outputs scores indicating how much each data point deviates from the norm. This is designed for anyone who needs to find anomalies in multivariate data for tasks like fraud detection, quality control, or system monitoring.
About pygod
pygod-team/pygod
A Python Library for Graph Outlier Detection (Anomaly Detection)
This tool helps you pinpoint unusual or suspicious activities and patterns within complex networked data, like social media connections or financial transaction graphs. You provide your graph data, and it identifies which nodes (individuals, transactions) or edges (connections) stand out as anomalies. It's designed for data analysts and security professionals who need to detect fraud, identify compromised accounts, or find rare events in interconnected systems.
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