Finding groups of similar data in an unsupervised method, called clustering, is a fundamental problem in data science. When working with data in the form of a graph, we often consider an edge as an indicator of similarity between two nodes, and clustering (or community detection) involves finding sets of nodes that have many edges between them. Unfortunately, there is not a single definition of what makes a community, which allows for a myriad of community detection algorithms. Furthermore, most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). In this talk, we review the Hierarchical Single Linkage Clustering algorithm used in HDBSCAN, and test its application to graph clustering with outliers.