Conway's Law describes that software systems are structured according to the communication structures of their developers. These developers when working on a feature or correcting a bug commit together a set of source code artifacts. The analysis of these co-changes makes it possible to identify change dependencies between artifacts. Influenced by Conway's Law, we hypothesize that Structural Hole Metrics (SHM) are able to identify strong and weak change coupling. We used SHM computed from communication networks to predict co-changes among files. Comparing SHM against process metrics using six well-known classification algorithms applied to Rails and Node.js projects, we achieved recall and precision values near 80% in the best cases. Mathews Correlation metric was used to verify if SHM was able to identify strong and weak co-changes. We also extracted rules to provide insights about the metrics using classification tree. To the best of our knowledge, this is the first study that investigated social aspects to predict change dependencies and the results obtained are very promising.