https://github.com/kristyspatel/Netsimile-Anomaly-Detection-in-time-series-graphs-ML

Implementation of netsimile Algorithm to detect anomalies in time series graphs.
https://github.com/kristyspatel/Netsimile-Anomaly-Detection-in-time-series-graphs-ML

Last synced: 3 months ago
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Implementation of netsimile Algorithm to detect anomalies in time series graphs.

• Host: GitHub
• URL: https://github.com/kristyspatel/Netsimile-Anomaly-Detection-in-time-series-graphs-ML
• Owner: kristyspatel
• Created: 2015-03-10T17:45:44.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-03-10T17:46:38.000Z (over 9 years ago)
• Last Synced: 2023-10-20T21:03:59.318Z (9 months ago)
• Language: Python
• Size: 1.73 MB
• Stars: 9
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.txt

Lists

• benedekrozemberczki-awesome-graph-classification - [Python

README

        
Implementation of Netsimile algorithm to detect anomalous graphs in Time series graphs. 

Link to paper: http://arxiv.org/pdf/1209.2684.pdf

(a) Software that needs to be installed (if any) with URL’s to download it from 

and instructions on how to install them.

Anaconda Python 2.7: http://continuum.io/downloads

Download "Windows 64-Bit Python 2.7 Graphical Installer" from above link

IGraph Python Module: http://www.lfd.uci.edu/~gohlke/pythonlibs/#python-igraph

Download the python-igraph-0.7.0.win-amd64-py2.7.exe file from the above link and install. 

windows powershell

(b) Environment variable settings (if any) and OS it should/could run on.

The program has been tested on Windows 8.0 (64bit)

The program is written in pycharm IDE and executed in pyCharm.

(c) Instructions on how to run the program

The folder where the program is executed should have graph and anomalies folders as the program outputs the graph in graph folder and anomalies.txt in anomalies folder

Change the directory to the project folder which has the Netsimile.py file , graph folder and anomalies folder.

> cd 

> ls

Netsimile.py graph anomalies

Execute the following command:

>python Netsimile.py  

E.g. 

>python Netsimile.py C:/Users/Kristy/PycharmProjects/Netsimile/input_files enron_test

The above command will produce enron_test.txt in anomalies folder and enron-test.png on graph folder.

(d) Instructions on how to interpret the results.

The results are saved in graph and anomalies folder

The output text files are stored in anomalies folder. The output file shows the threshold, number of anomalies and the anomalous time points on each line. 

(e) Sample input files are stored in input_files folder and output files are in anomalies folder while graphs are stored in graph folder in the zip file submitted.