Event logs are widely used to record the status of high-tech systems, making log anomaly detection important for monitoring those systems. We propose a graph-based method for unsupervised log anomaly detection, dubbed \textit{Logs2Graphs}, which first converts event logs into attributed, directed, and weighted graphs, and then leverages graph neural networks to perform graph-level anomaly detection. Specifically, we introduce OCDiGCN, a novel graph neural network model for detecting graph-level anomalies in a collection of attributed, directed, and weighted graphs. By coupling the graph representation and anomaly detection steps, OCDiGCN can learn a representation that is especially suited for anomaly detection, resulting in a high detection accuracy. For each detected anomaly, we provide a subset of nodes that are crucial in OCDiGCN’s predictions, offering useful insights for root cause diagnosis. Experiments on five benchmark datasets show that \textit{Logs2Graphs} matches or exceeds current top log anomaly detection methods on simple datasets and largely outperforms them on complex ones.