1. A method for selecting representative metrics from which anomalous metrics data is determined, the method comprising: accessing metrics datasets, wherein each metrics dataset includes data values for a respective metric indicating activity over a data network; generating, by a processing device, a data graph comprising nodes and edges, wherein each node represents a respective one of the metrics from one of the metrics datasets; grouping, by the processing device, the metrics datasets into clusters based on, for each of the clusters, a similarity of data values in a respective pair of datasets from the metrics datasets, the similarity determined based on the edges of the data graph; determining, by the processing device, principal component datasets for the clusters, wherein, for each cluster, a respective principal component dataset comprises a linear combination of a respective subset of the metrics datasets; selecting, by the processing device, representative nodes from the data graph, wherein each representative node represents a respective metrics dataset having a highest contribution, for grouped metrics datasets in a respective cluster, to a respective principal component dataset for the respective cluster; executing, by the processing device, an anomaly detection that is restricted to particular metrics datasets corresponding to the selected representative nodes; determining, by the processing device and based on the anomaly detection, that a metrics dataset from the particular metrics datasets includes anomalous data; identifying, by the processing device and based on the data graph, additional metrics datasets from one of the clusters that includes the metrics dataset having the anomalous data; and outputting, by the processing device, an indicator that additional anomalous data is included in the additional metrics datasets.