Monitoring high performance computing systems has become increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on ever larger machines. We present a method for anomaly detection on syslog data, one of the most important data streams for determining system health. Syslog messages pose a difficult question for analysis because they include a mix of structured natural language text as well as numeric values. We present an anomaly detection framework that combines graph analysis, relational learning, and kernel density estimation to detect unusual syslog messages. We design an event block detector, which finds groups of related syslog messages, to retrieve the entire section of syslog messages associated with a single anomalous line. Our novel approach successfully retrieves anomalous behaviors inserted into syslog files from a virtual machine, including messages indicating serious system problems. We also test our approach on syslog messages from the Trinity supercomputer and find that our methods do not generate significant false positives.