  Deep learning models are now being increasingly used for predictive process
mining tasks in business processes. Modern approaches have been successful in
achieving better performance for different predictive tasks, as compared to
traditional approaches. 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. The results obtained from the
experiments, significantly improves over the previously reported results for
most of the individual tasks. Interestingly, it was observed that a linear
Multi-Layer Perceptron (MLP) with dropout was able to outperform the GCN
variants in both the prediction tasks. Using a quartile-based analysis, it was
further observed that the other variants were able to perform better than MLP
at individual quartiles in some of the tasks where the MLP had the best overall
performance.
