K-Clique-Graphs for Dense Subgraph Discovery
Abstract
Finding dense subgraphs in a graph is a fundamental graph mining task, with applications in several fields. Algorithms for identifying dense subgraphs are used in biology, in finance, in spam detection, etc. Standard formulations of this problem such as the problem of finding the maximum clique of a graph are hard to solve. However, some tractable formulations of the problem have also been proposed, focusing mainly on optimizing some density function, such as the degree density and the triangle density. However, maximization of degree density usually leads to large subgraphs with small density, while maximization of triangle density does not necessarily lead to subgraphs that are close to being cliques.
Cite
Text
Nikolentzos et al. "K-Clique-Graphs for Dense Subgraph Discovery." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71249-9_37Markdown
[Nikolentzos et al. "K-Clique-Graphs for Dense Subgraph Discovery." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/nikolentzos2017ecmlpkdd-kcliquegraphs/) doi:10.1007/978-3-319-71249-9_37BibTeX
@inproceedings{nikolentzos2017ecmlpkdd-kcliquegraphs,
title = {{K-Clique-Graphs for Dense Subgraph Discovery}},
author = {Nikolentzos, Giannis and Meladianos, Polykarpos and Stavrakas, Yannis and Vazirgiannis, Michalis},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2017},
pages = {617-633},
doi = {10.1007/978-3-319-71249-9_37},
url = {https://mlanthology.org/ecmlpkdd/2017/nikolentzos2017ecmlpkdd-kcliquegraphs/}
}