Publications

TAESPM: A Learning-Based Spatiotemporal Prediction Framework for Dynamic Influence Maximization

Published in Submitted to IEEE Transactions on Network Science and Engineering (TNSE), 2025

This paper proposes TAESPM, a learning-based spatiotemporal prediction framework designed to overcome the “Myopic Trap” in dynamic influence maximization by capturing long-range dependencies.

Recommended citation: Lvyizhuo, et al. (2025). "TAESPM: A Learning-Based Spatiotemporal Prediction Framework for Dynamic Influence Maximization." (Under Review in IEEE TNSE).
Download Paper