DescriptionExecution time of an application is affected by several performance parameters, e.g. number of threads, decomposition, etc. Hence, an important problem in high performance computing is to study the influence of these parameters on the performance of an application. Additionally, quantifying the influence of individual parameter configurations (data samples) on performance also aids in identifying sub-domains of interest in high-dimensional parameter spaces. Conventionally, such analysis is performed using a surrogate model, which introduces its own bias that is often non-trivial to undo, leading to inaccurate results. In this work, we propose an entirely data-driven, model-agnostic influence analysis approach based on recent advances in analyzing functions on graphs. We show that the problem of identifying influential parameters (features) and configurations (samples) can be effectively addressed within this framework.