FelixDJC/NLGAD
An official source code for paper "Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning", accepted by ACM MM 2023.
This project helps data scientists and researchers identify unusual or suspicious patterns within complex networked data, like social networks or transaction graphs. It takes graph-structured data as input and outputs a determination of which nodes or connections are anomalous, helping to flag unusual behaviors. Data analysts working with interconnected datasets would find this useful.
No commits in the last 6 months.
Use this if you need to detect anomalies within large, complex graph datasets and want a method focused on learning what 'normal' behavior looks like.
Not ideal if your data isn't structured as a graph or if you require real-time, low-latency anomaly detection on streaming data.
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Language
Python
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Last pushed
Dec 01, 2023
Commits (30d)
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