Researchers draw abstract inferences about groups of similar networks by using graph topology metrics to summarize structural features. However, networks describing real-world phenomena often contain inaccuracies, as challenges gathering data can lead to some interactions being missed. Thus, it is important to understand how much different structural metrics are affected by missed links. To address this question, we measured six different topological properties on a collection of ecological networks: number of connected components, variance in node betweenness and PageRank, largest Eigenvalue, the number of non-zero Eigenvalues, and community detection as determined by four different algorithms. Using a database of bipartite ecological interaction networks, we then tested how these properties change as additional edges – representing data that may have been missed – are added to the networks. We found substantial variation in how robust different properties were to the missed links. These results provide a foundation for selecting network properties to use when analyzing messy network data. Moreover, we believe this technique should generalize well to other network types.