In collaborative software development, developers generally make code changes and commit the changes to the repositories. Among others, “making small, single-purpose commits” is considered the best practice for making commits, allowing the team to quickly understand the code changes. Rather than following the best practice, developers often make \emph{tangled commits}, which wrap code changes that implement different purposes. Such commits make it difficult for other developers to understand the code changes when conducting subsequent development. Early works on untangling code changes rely on human-specified heuristic rules or features, do not consider context, and are labor-intensive. Recent works model the the local context of code changes as a graph at the statement level, with statements as nodes and code dependencies as edges, and then cluster the changed statements. However, recent works ignore the hidden dependencies in the global context, e.g. a pair of tangled code changes may have no code dependency, and a pair of untangled code changes may have obvious code dependency. To solve this problem, we focus on detecting hidden dependencies among code changes. We model the global context of code changes as graphs at finer-grained, hierarchical levels, i.e., at both entity and statement levels. Then we propose a Heterogeneous Directed Graph Neural Network (\emph{HD-GNN}) to detect hidden dependencies among code changes by aggregating the global context in both connected or disconnected entity-level subgraphs that intersected with the code changes. Evaluation of common C # and Java datasets with 1,612 and 14k \emph{tangled commits} and manually validated datasets (MVD) with 600 commits show that \emph{HD-GNN} achieves an average enhancement of effectiveness of 25$%$ and 19.2$%$ compared to existing approaches and far superior to existing approaches in MVD, without sacrificing time efficiency.