This document proposes two new algorithms, L-SHAPLEY and C-SHAPLEY, for interpreting black-box machine learning models in an instance-wise and model-agnostic manner. L-SHAPLEY and C-SHAPLEY are approximations of the SHAPLEY value that take graph structure between features into account to improve computational efficiency. The algorithms were evaluated on text and image classification tasks and were shown to outperform baselines like KERNELSHAP and LIME, providing more accurate feature importance scores according to both automatic metrics and human evaluation.