Collaborator Recommendation Based on Dynamic Attribute Network Representation Learning

Scientific collaboration plays an important role in modern academic research. Collaborations between scholars will bring about high-quality papers and improve the academic influence of scholars. However, it is more and more difficult to find a suitable collaborator due to the rapid growth of academic data. There are already some recommendation systems based on calculating the similarity between scholars. But most of them do not consider the dynamic nature of the scientific collaboration network. To this end, we propose a collaborator recommendation algorithm based on dynamic attribute network representation learning (DANRL). It takes advantage of the network topology, scholar attributes and the dynamic nature of the network to represent scholars as low-dimensional vectors. By calculating the cosine similarity between scholar vectors, we can recommend the most similar collaborators to target scholars. Moreover, at each time step of the dynamic network, our method only needs to train embedding vectors for some selected nodes instead of performing random walks and training embedding vectors for all nodes, which can significantly improve the recommendation efficiency. Experiments on two real-world datasets show that DANRL outperforms several baseline methods.