TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks
Abstract
This paper presents a system for tracking and analyzing the evolution and transformation of topics in an information network. The system consists of four main modules for pre-processing, adaptive topic modeling, network creation and temporal network analysis. The core module is built upon an adaptive topic modeling algorithm adopting a sliding time window technique that enables the discovery of groundbreaking ideas as those topics that evolve rapidly in the network.
Cite
Text
Bioglio et al. "TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71273-4_46Markdown
[Bioglio et al. "TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/bioglio2017ecmlpkdd-tranet/) doi:10.1007/978-3-319-71273-4_46BibTeX
@inproceedings{bioglio2017ecmlpkdd-tranet,
title = {{TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks}},
author = {Bioglio, Livio and Pensa, Ruggero G. and Rho, Valentina},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {432-436},
doi = {10.1007/978-3-319-71273-4_46},
url = {https://mlanthology.org/ecmlpkdd/2017/bioglio2017ecmlpkdd-tranet/}
}