Before revision:  In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next activity in a given process instance. We have introduced a new method for representing feature vectors for any individual event in a given process instance, taking into consideration the structure of Directly-follows process graphs generated from the corresponding datasets. The adjacency matrix of the process graphs generated has been used as input to a Graph Convolutional Network (GCN). Different model variants make use of variations in the representation of the adjacency matrix. The performance of all the model variants have been tested at different stages of a process, determined by quartiles estimated based on the number of events and  the case duration.
After revision:  In this work, five different variants of a model involving a Graph Convolutional Layer and linear layers have been tested for the task of predicting the nature and timestamp of the next process activity. In contrast to existing studies, we evaluate our models' performance at different stages of a process, determined by quartiles estimated based on the number of events and the case duration.