Temporal Network Prediction and Interpretation

Temporal networks refer to networks like physical contact networks whose topology changes over time. Predicting future temporal network is crucial e.g., to forecast the epidemics. Existing prediction methods are either relatively accurate but black-box, or white-box but less accurate. The lack of interpretable and accurate prediction methods...

Task-Aware Connectivity Learning for Incoming Nodes Over Growing Graphs

Data processing over graphs is usually done on graphs of fixed size. However, graphs often grow with new nodes arriving over time. Knowing the connectivity information of these nodes, and thus, the expanded graph is crucial for processing data over the expanded graph. In its absence, its inference and the subsequent data processing become...