  The world around us consists of objects that have different relationships
with each other. The result of these communications is various networks, part
of which are bipartite networks. While many studies have investigated essential
network members, less attention has been paid to the bipartite graphs. On the
other hand, one of the most critical aspects of network analysis is the
detection and extraction of communities that arise in the structure of
networks. For these reasons, we have introduced a measure called H.H to
identify influential nodes in community formation in the one-mode projection of
a bipartite graph. The three main parameters that influence this measure are
the size of the formed community, the effect of each node in the formation of
that community, and the number of communities in which the node had an impact.
The results of this paper show the differences of this measure with other
centralities (eigenvector centrality, closeness centrality, betweenness
centrality, and degree centrality) and how this measure takes into account
aspects that other centralities do not. Through H.H score, we can find
essential nodes that have been effective in forming a community, and by
removing these nodes, communities can be eliminated. Any of the existing
centralities has not addressed this issue, and this measure has sufficient
independence to represent the important nodes in the formation of the
communities. Experimental validation of the proposed measure is carried out on
two real-world datasets: Southern Women Network and Person-Crime Network. The
results of the implementation of the H.H score on the Person-Crime dataset show
that by eliminating the nodes with the highest H.H score (top-10%), 29% of the
communities have changed; this is while the centralities change the average of
18% of the communities and this shows the importance of the H.H score.
