Link prediction in temporal networks is relevant for many real-world systems, however, current approaches are usually characterized by high computational costs. The authors propose a temporal link prediction framework based on the sequential stacking of static network features, for improved computational speed, appropriate for temporal networks with completely unobserved or partially observed target layers.
- Xie He
- Amir Ghasemian
- Peter J. Mucha