Bipartite graph analysis as an alternative to reveal clusterization in complex systems
Digital Libraries
2020-03-24 v1 Physics and Society
Abstract
We demonstrate how analysis of co-clustering in bipartite networks may be used as a bridge to connect, compare and complement clustering results about community structure in two different spaces: single-mode bipartite network projections. As a case study we consider scientific knowledge, which is represented as a complex bipartite network of articles and related concepts. Connecting clusters of articles and clusters of concepts via article-to-concept bipartite co-clustering, we demonstrate how concept features (e.g. subject classes) may be inferred from the article ones.
Cite
@article{arxiv.1806.04406,
title = {Bipartite graph analysis as an alternative to reveal clusterization in complex systems},
author = {Vasyl Palchykov and Yurij Holovatch},
journal= {arXiv preprint arXiv:1806.04406},
year = {2020}
}
Comments
5 pages, 2 figures, submitted to IEEE Second International Conference Data Stream Mining & Processing (Dsmp2018)