@incollection{statphys23_1027,
	title = {Network Clustering by Adjacency Matrix Reordering},
	address = {Genova, Italy},
	author = {A.K. Krueger},
	booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics},
	editor = {Luciano Pietronero and Vittorio Loreto and Stefano Zapperi},
	month = {9-13 July},
	url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1027},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/214b3f3b14f4d98c29c1452b57b0ddfe5/statphys23},
	abstract = {We present a deterministic approach for clustering weighted networks, by sorting the adjacency matrix by a line difference metrics. 
This parametrized metrics takes into account the direct connection of nodes, the structural equivalence ($N_1$ -neighbourhood), and the similarity of the $N_2$ -neighbourhood. 
For each such 2-parameter pair, the resulting clustering is an ordered adjacency matrix plus the optimal positions where to cut into subspaces; the final clustering is the one with the overall highest modularity.
Looking at networks as matrices enables new views; one outcome was this algorithm, it produces results that are good to interpret. However, due to the full metrics between all lines and the exhaustive search in parameter space, it is not very fast yet.},
	keywords = {adjacency algorithm clustering matrix statphys23 topic-11 weighted }
}