  Using an intuitive concept of what constitutes a meaningful community, a
novel metric is formulated for detecting non-overlapping communities in
undirected, weighted heterogeneous networks. This metric, modularity density,
is shown to be superior to the versions of modularity density in present
literature. Compared to the previous versions of modularity density,
maximization of our metric is proven to be free from bias and better detect
weakly-separated communities particularly in heterogeneous networks. In
addition to these characteristics, the computational running time of our
modularity density is found to be on par or faster than that of the previous
variants. Our findings further reveal that community detection by maximization
of our metric is mathematically related to partitioning a network by
minimization of the normalized cut criterion.
