  Research into detection of dense communities has recently attracted
increasing attention within network science, various metrics for detection of
such communities have been proposed. The most popular metric -- Modularity --
is based on the so-called rule that the links within communities are denser
than external links among communities, has become the default. However, this
default metric suffers from ambiguity, and worse, all augmentations of
modularity and based on a narrow intuition of what it means to form a
"community". We argue that in specific, but quite common systems, links within
a community are not necessarily more common than links between communities.
Instead we propose that the defining characteristic of a community is that
links are more predictable within a community rather than between communities.
In this paper, based on the effect of communities on link prediction, we
propose a novel metric for the community detection based directly on this
feature. We find that our metric is more robustness than traditional
modularity. Consequently, we can achieve an evaluation of algorithm stability
for the same detection algorithm in different networks. Our metric also can
directly uncover the false community detection, and infer more statistical
characteristics for detection algorithms.
