Effective Visualization of Information Diffusion Process over Complex Networks
Abstract
Effective visualization is vital for understanding a complex network, in particular its dynamical aspect such as information diffusion process. Existing node embedding methods are all based solely on the network topology and sometimes produce counter-intuitive visualization. A new node embedding method based on conditional probability is proposed that explicitly addresses diffusion process using either the IC or LT models as a cross-entropy minimization problem, together with two label assignment strategies that can be simultaneously adopted. Numerical experiments were performed on two large real networks, one represented by a directed graph and the other by an undirected graph. The results clearly demonstrate the advantage of the proposed methods over conventional spring model and topology-based cross-entropy methods, especially for the case of directed networks.
Cite
Text
Saito et al. "Effective Visualization of Information Diffusion Process over Complex Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_22Markdown
[Saito et al. "Effective Visualization of Information Diffusion Process over Complex Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/saito2008ecmlpkdd-effective/) doi:10.1007/978-3-540-87481-2_22BibTeX
@inproceedings{saito2008ecmlpkdd-effective,
title = {{Effective Visualization of Information Diffusion Process over Complex Networks}},
author = {Saito, Kazumi and Kimura, Masahiro and Motoda, Hiroshi},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {326-341},
doi = {10.1007/978-3-540-87481-2_22},
url = {https://mlanthology.org/ecmlpkdd/2008/saito2008ecmlpkdd-effective/}
}