  There are several metrics (Modularity, Mutual Information, Conductance, etc.)
to evaluate the strength of graph clustering in large graphs. These metrics
have great significance to measure the effectiveness and they are often used to
find the strongly connected clusters with respect to the whole graph. In this
paper, we propose a new metric to evaluate the strength of graph clustering and
also study its applications. We show that our proposed metric has great
consistency which is similar to other metrics and easy to calculate. Our
proposed metric also shows consistency where other metrics fail in some special
cases. We demonstrate that our metric has reasonable strength while extracting
strongly connected communities in both simulated (in silico) data and real data
networks. We also show some comparative results of our proposed metric with
other popular metric(s) for Online Social Networks (OSN) and Gene Regulatory
Networks (GRN).
