NETEVOLVE: Social Network Forecasting using Multi-Agent Reinforcement Learning with Interpretable Features

Predicting how social networks change in the future is important in many applications. Results in social network research have shown that the change in the network can be explained by a small number of concepts, such as "homophily" and "transitivity". However, existing prediction methods require many latent features that are not connected to such concepts, making the methods' black boxes and their prediction results difficult to interpret, making them harder to derive scientific knowledge about social networks. In this study, we propose NetEvolve a novel multi-agent reinforcement learning-based method that predicts changes in a given social network. Given a sequence of changes as training data, NetEvolve learns the characteristics of the nodes with interpretable features, such as how the node feels rewards for connecting with similar people and the cost of the connection itself. Based on the learned feature, NetEvolve makes a forecast based on multi-agent simulation. The method achieves comparable or better accuracy than existing methods in predicting network changes in real-world social networks while keeping the prediction results interpretable.