FelixDJC/GRADATE
An official source code for paper "Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View", accepted by AAAI 2023.
This project helps researchers and data scientists identify unusual patterns or behaviors within complex network data. It takes in graph datasets, which represent connections between entities (like social networks, financial transactions, or biological interactions), and outputs a list of 'anomalous' nodes that stand out from the rest. This is useful for anyone working with interconnected data who needs to detect outliers or suspicious activities.
No commits in the last 6 months.
Use this if you need to detect anomalies within graph-structured data, such as identifying fraudulent transactions in a financial network, discovering unusual user behavior in a social network, or flagging abnormal events in a logistics network.
Not ideal if your data is not in a graph format or if you are looking for simple statistical outliers in tabular data rather than complex relational anomalies.
Stars
66
Forks
6
Language
Python
License
—
Category
Last pushed
Dec 01, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/FelixDJC/GRADATE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
elki-project/elki
ELKI Data Mining Toolkit
raphaelvallat/antropy
AntroPy: entropy and complexity of (EEG) time-series in Python
Minqi824/ADBench
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.