Speaker:

Weili (Lily) Wu (The University of Texas at Dallas)

Time:

• 11:00-12:00 (Time in Beijing)
• June 11, 2024 (Tuesday)

Venue:

518

Abstract:

Online social platforms have become more and more popular, and the dissemination of information on social networks has attracted wide attention of the industries and academia. Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied. Since it is #P-hard to compute the influence spread given a seed set, the state-of-art methods, including heuristic and approximation algorithms, faced with great difficulties such as theoretical guarantee, time efficiency, generalization, etc. This makes it unable to adapt to large-scale networks and more complex applications. With the latest achievements of Deep
Reinforcement Learning (DRL) in artificial intelligence and other fields, a lot of works has focused on exploiting DRL to solve the combinatorial optimization problems. Inspired by this, we propose a novel end-to-end DRL framework, ToupleGDD, to address the IM problem which incorporates three coupled graph neural networks for network embedding and double deep Q-networks for parameters learning. Previous efforts to solve the IM problem with DRL trained their models on the subgraph of the whole network, and then tested their performance on the whole graph, which makes the performance of their models unstable among different networks. However, our model is trained on several small randomly generated graphs and tested on completely different networks, and can obtain results that are very close to the state-of-the-art methods. In addition, our model is trained with a small budget, and it can perform well under various large budgets in the test, showing strong generalization ability. Finally, we conduct extensive experiments on synthetic and realistic datasets, and the experimental results prove the effectiveness and superiority of our model.

Speaker Bio:

Dr. Weili (Lily) Wu received her MS and PhD degrees in computer science both from University of Minnesota, in 1998 and 2002 respectively. She is currently a full professor and a lab director of the Data Communication and Data Management (DCDM) Laboratory at the Department of Computer Science and Engineering, the University of Texas at Dallas. Her research interest is mainly in Big Data, Social Network, Blockchain Technology, wireless sensor network, IoT, Data Mining. She has published more than 241 journal papers and 103 conference papers in various prestigious journals and conferences such as IEEE/ACM Transactions on Networking, IEEE Trans. Netw. Sci. Eng., Comput. Social Systems, IoT Journal, ACM Transactions on Knowledge Discovery in Data, IEEE Trans. Reliability, IEEE TKDE, Multimedia, ACM Transaction on Sensor Networks (TOSN), IEEE Trans. Netw. Serv. Manag., Wirel. Commun., Mob. Comput., Parallel Distrib. Syst., IEEE ICDCS, INFOCOM, ACM SIGKDD, etc. I’m an associate editor of IJBRA, Computational Social Networks (CSN), SOP Transactions on Wireless Communications (STOWC), DMAA, Journal of
Combinatorial Optimization (JOCO), and Journal of Global Optimization (JOGO).
She is an associate editor of International Journal of Bioinformatics Research and
Applications (IJBRA), Computational Social Networks (CSN), SOP Transactions on Wireless Communications (STOWC), DMAA, Journal of Combinatorial Optimization (JOCO), and Journal of Global Optimization (JOGO).