Log analysis is a critical technique for diagnosing issues in large-scale moderncomputing systems. Over the past decades, numerous deep learning-based log analysis approaches have been developed to detect system anomalies reflected in log data. Anomalies in logs generally fall into two categories: event-level semantic anomalies and structural anomalies. Event-level semantic anomalies occur within the textual content of individual log events, while structural anomalies arise from violations of quantitative relational patterns or sequential dependencies in log event sequences. Existing log anomaly detection methods can be broadly categorized into Large Language Models (LLMs) and Graph Neural Networks (GNNs) approaches. GNNs excel at detecting structural anomalies by capturing spatial structural relationships, while LLMs are particularly effective at identifying event-level semantic anomalies due to their strong contextual understanding ability.