Balanced Team Formation Using Hybrid Graph Convolution Networks and MILP

In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an Integer Programming solver. The model utilizes the potential of graph neural networks, which have the ability to capture complex relationships and preferences among nodes. While the graph neural network forms node embeddings that are fed as input into the next layer of the model, the introduced MILP solver works to solve the team formation problem. Finally, our experimental work shows that the outcome of the model is balanced teams.