  Several algorithms have been proposed to filter information on a complete
graph of correlations across stocks to build a stock-correlation network. Among
them the planar maximally filtered graph (PMFG) algorithm uses $3n-6$ edges to
build a graph whose features include a high frequency of small cliques and a
good clustering of stocks. We propose a new algorithm which we call
proportional degree (PD) to filter information on the complete graph of
normalised mutual information (NMI) across stocks. Our results show that the PD
algorithm produces a network showing better homogeneity with respect to
cliques, as compared to economic sectoral classification than its PMFG
counterpart. We also show that the partition of the PD network obtained through
normalised spectral clustering (NSC) agrees better with the NSC of the complete
graph than the corresponding one obtained from PMFG. Finally, we show that the
clusters in the PD network are more robust with respect to the removal of
random sets of edges than those in the PMFG network.
