Network motifs are subnetworks that appear in the network far more frequently than in randomized networks. They have gathered much attention for uncovering structural design principles of complex networks. One of the previous approaches for motif detection is sampling method, in- troduced to perform the computational challenging task. However, it suﬀers from sampling bias and probability assignment. In addition, subgraph search, being very time-consuming, is a critical process in motif detection as we need to enumerate subgraphs of given sizes in the original input graph and an ensemble of random generated graphs. Therefore, we present a Degree-based Sampling Method with Partition-based Subgraph Finder for larger motif detection. Inspired by the intrinsic feature of real biological networks, Degree-based Sampling is a new solution for probability assignment based on degree. And, Partition-based Subgraph Finder takes its inspiration from the idea of partition, which improves computational eﬃciency and lowers space consumption. Experimental study on UETZ and E.COLI data set shows that the proposed method achieves more accuracy and eﬃciency than previous methods and scales better with increasing subgraph size.