Time Series Link Prediction Using NMF

Abstract

Data in many fields such as e-commerce, social networks, and web data can be modeled as bipartite graphs, where a node represents a person and/or an object and a link represents the relationship between people and/or objects. Since the relationships change with time, data mining techniques for time series graphs have been actively studied. In this paper, we study the problem of predicting links in the future graph from historical graphs. Although various studies have been carried out on link prediction, the prediction accuracy of existing methods is still low because it is difficult to capture continuous change with time. Therefore, we propose a new method that combines non-negative matrix factorization (NMF) and a time series data forecasting method. NMF extracts the latent features while the forecasting method captures and predicts the changes of the features with time. Our method can predict hidden links that do not appear in historical graphs. Our experiments with real datasets show that our method has a higher prediction accuracy compared to existing methods.