DANCer: Dynamic Attributed Network with Community Structure Generator
Abstract
We propose a new generator for dynamic attributed networks with community structure which follow the known properties of real-world networks such as preferential attachment, small world and homophily. After the generation, the different graphs forming the dynamic network as well as its evolution can be displayed in the interface. Several measures are also computed to evaluate the properties verified by each graph. Finally, the generated dynamic network, the parameters and the measures can be saved as a collection of files.
Cite
Text
Benyahia et al. "DANCer: Dynamic Attributed Network with Community Structure Generator." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_9Markdown
[Benyahia et al. "DANCer: Dynamic Attributed Network with Community Structure Generator." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/benyahia2016ecmlpkdd-dancer/) doi:10.1007/978-3-319-46131-1_9BibTeX
@inproceedings{benyahia2016ecmlpkdd-dancer,
title = {{DANCer: Dynamic Attributed Network with Community Structure Generator}},
author = {Benyahia, Oualid and Largeron, Christine and Jeudy, Baptiste and Zaïane, Osmar R.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {41-44},
doi = {10.1007/978-3-319-46131-1_9},
url = {https://mlanthology.org/ecmlpkdd/2016/benyahia2016ecmlpkdd-dancer/}
}