It is difficult to detect and evaluate the number of communities in complex networks, especially when the situation involves with an ambiguous boundary between the inner- and inter-community densities. In this paper, Discrete Nodal Domain Theory could be used to provide a criterion to determine how many communities a network would have and how to partition these communities by means of the topological structure and geometric characterization. By capturing the signs of certain Laplacian eigenvectors we can separate the network into several reasonable clusters. The method leads to a fast and effective algorithm with application to a variety of real networks data sets.