Network motifs have been demonstrated to be the building blocks in many biological networks such as transcriptional regulatory networks. Finding network motifs plays a key role in understanding system level functions and design principles of molecular interactions. In this paper, we present a novel definition of the neighborhood of a node. Based on this concept, we formally define and present an effective algorithm for finding network motifs. The method seeks a neighborhood assignment for each node such that the induced neighborhoods are partitioned with no overlap. We then present a parallel algorithm to find network motifs using a parallel cluster. The algorithm is applied on an E. coli transcriptional regulatory network to find motifs with size up to six. Compared with previous algorithms, our algorithm performs better in terms of running time and precision. Based on the motifs that are found in the network, we further analyze the topology and coverage of the motifs. The results suggest that a small number of key motifs can form the motifs of a bigger size. Also, some motifs exhibit a correlation with complex functions. This study presents a framework for detecting the most significant recurring subgraph patterns in transcriptional regulatory networks.