Learning Networks with Attention Layers for Team Recommendation

The Team Formation Problem aims to identify a group of experts who possess the required skills to complete a common goal. Graph-based approaches have been commonly used to solve this problem, but recently, researchers have started exploring this problem from the perspective of social information retrieval and applying neural architectures to recommend teams of experts. However, the learning process of these architectures is faced with several challenges. This includes the inability to handle network modifications after the training process is over as well as the time complexity of the learning process is high, which is proportional to the size of the network. In this study, we propose a new framework called “LANT - Leveraging Graph Attention Network for Team formation” which leverages graph neural networks and variational inference to address the challenges faced by existing approaches. The proposed framework utilizes transfer learning and neural team recommendation, with self-supervised learning of node embeddings achieved using Deep Graph Infomax with Graph Attention Networks as an encoder. We demonstrate empirically how LANT effectively addresses the challenges faced by existing approaches and outperforms state-of-the-art methods on large scale real world datasets. The proposed framework provides an efficient and scalable solution to team formation problems and can be applied in various fields where expert teams are required to achieve a common goal