Engineers utilize logs as a primary resource for fault localization in large-scale software and system testing, a process that is notoriously time-consuming, costly, and labor-intensive. Despite considerable progress in automated fault localization approaches, their applicability remains limited in such settings, due to the unavailability of fine-grained features in logs essential for most existing fault localization methods. In response, we introduce FALCON, a novel log-based fault localization framework. FALCON organizes complex semantic log information into graphical representations and employs contrastive learning to capture the differences between passed and failed logs, enabling the identification of crucial fault-related features. It also incorporates a specifically designed transitive analysis-based adaptive graph augmentation to minimize the influence of fault-unrelated log information on contrastive learning. Through extensive evaluations against 34 spectrum-based and 4 learning-based fault localization methods, FALCON demonstrates superior performance by outperforming all the methods in comparison. In addition, FALCON demonstrated its practical value by successfully identifying 71 out of 90 faults with a file-level Top-1 accuracy rate during a one-month deployment within a global company’s testing system.