NEDRL-CIM:Network Embedding Meets Deep Reinforcement Learning to Tackle Competitive Influence Maximization on Evolving Social Networks

Khurshed Ali, Chih Yu Wang, Mi Yen Yeh, Cheng Te Li, Yi Shin Chen

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

Competitive Influence Maximization (CIM) aims to maximize the influence of a party given the competition from other parties in the same social network, like companies find key users to promote their competitive products on the social network to achieve maximum profit. Recently, learning-based solutions are introduced to tackle the competitive influence maximization problem. However, such studies focus on the static nature of social networks. This paper proposes a deep reinforcement learning-based framework employing network embedding, termed as DRL-EMB, to tackle the CIM problem on evolving social networks. The DRL-EMB key objective is to find the best strategy to maximize the party’s reward, considering budget and competition with information propagation and network evolving being run in parallel. We validate our proposed framework with the DRL-based model using hand-crafted state features (DRL-HCF) and heuristic-based methods. Experimental results show that our proposed framework, DRL-EMB, achieves better results than heuristic-based and DRL-HCF models while significantly outperforming the DRL-HCF model in terms of time efficiency.

原文English
主出版物標題2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665420990
DOIs
出版狀態Published - 2021
事件8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021 - Virtual, Online, Portugal
持續時間: 2021 10月 62021 10月 9

出版系列

名字2021 IEEE 8th International Conference on Data Science and Advanced Analytics, DSAA 2021

Conference

Conference8th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2021
國家/地區Portugal
城市Virtual, Online
期間21-10-0621-10-09

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 訊號處理
  • 資訊系統與管理
  • 統計、概率和不確定性

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