stephaneseng/dynamic-network-visualization-boost-graph-library

Generate dynamic networks visualizations using the Boost Graph Library

dynamic-network-visualization-boost-graph-library

Generate dynamic networks visualizations using the Boost Graph Library.

This project has been started as an alternative to StephaneSeng/dynamic-network-visualization-d3.

Getting started

0. Requirements

• OpenGL 2
• libboost-date-time-dev
• libboost-graph-dev
• libboost-program-options-dev
• libglfw3
• libglfw3-dev

1. Data preparation

The doc/vertices.csv and doc/edges.csv CSV files are given as an example of the expected file format.

Note that:

• These files have been generated from the Bitcoin Alpha trust weighted signed network distributed by the Stanford Network Analysis Project
• The file MUST start with an header
• Entries MUST be sorted by time, by chronological order

2. Build

$ make

3. Run

Using the example dataset and for the events happening between 2010-11-01 and 2011-04-01, month per month:

$ ./build/main --vertices_file_path doc/vertices.csv --edges_file_path doc/edges.csv --start_date 2010-11-01 --end_date 2011-04-01

References

• J. Leskovec, A. Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data, 2014
• S. Kumar, F. Spezzano, V.S. Subrahmanian, C. Faloutsos. Edge Weight Prediction in Weighted Signed Networks. IEEE International Conference on Data Mining (ICDM), 2016
• S. Kumar, B. Hooi, D. Makhija, M. Kumar, V.S. Subrahmanian, C. Faloutsos. REV2: Fraudulent User Prediction in Rating Platforms. 11th ACM International Conference on Web Searchand Data Mining (WSDM), 2018