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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).
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Primary School Mathematics Teacher

Social Practice / Teaching, Xinyang Third Primary School, 2024

During this period, I served as a Mathematics teacher, gaining valuable experience in communicating complex concepts in a simplified manner.