Complex biological interaction networks contain several thousands of nodes and edges that represent interactions between entities such as transcription factors, microRNAs, and genes. The goal of network motif discovery is to detect small subgraphs or “network motifs” which are more likely to be observed in an experimentally verified network than a randomly chosen graph with the same in/out degree sequences. Network motif algorithms are typically heuristic in nature and aim to randomly sample the set of all possible graphs with fixed in and out degree sequences. As of yet, there has been little progress made in assessing how well these various heuristic algorithms perform in terms of uniformity and independence. I will present a new mathematical method, based on the “cut norm”, by which the performance of these heuristic algorithms can be assessed. This work-in-progress is joint with Molly Megraw and Mitra Ansari.