  Community detection is a significant and challenging task in network
research. Nowadays, plenty of attention has been focused on local methods of
community detection. Among them, community detection with a greedy algorithm
typically starts from the identification of local essential nodes called
central nodes of the network; communities expand later from these central nodes
by optimizing a modularity function. In this paper, we propose a new central
node indicator and a new modularity function. Our central node indicator, which
we call local centrality indicator (LCI), is as efficient as the well-known
global maximal degree indicator and local maximal degree indicator; on certain
special network structure, LCI performs even better. On the other hand, our
modularity function F2 overcomes certain disadvantages,such as the resolution
limit problem,of the modularity functions raised in previous literature.
Combined with a greedy algorithm, LCI and F2 enable us to identify the right
community structures for both the real world networks and the simulated
benchmark network. Evaluation based on the normalized mutual information (NMI)
suggests that our community detection method with a greedy algorithm based on
LCI and F2 performs superior to many other methods. Therefore, the method we
proposed in this paper is potentially noteworthy.
