Online Algorithm for Node Feature Forecasting in Temporal Graphs

In this paper, we propose an online algorithm "mspace" for forecasting node features in temporal graphs, which adeptly captures spatial cross-correlation among different nodes as well as the temporal autocorrelation within a node. The algorithm can be used for both probabilistic and deterministic multi-step forecasting, making it applicable for estimation and generation tasks. Comparative evaluations against various baselines, including graph neural network (GNN) based models and classical Kalman filters, demonstrate that mspace performs at par with the state-of-the-art and even …

algorithm correlation cs.dm cs.lg cs.sy eess.sy feature features forecasting graphs making node paper spatial temporal the algorithm