Leveraging transfer learning in reinforcement learning to tackle competitive influence maximization
Competitive influence maximization (CIM) is a key problem that seeks highly influential
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
Leveraging transfer learning in reinforcement learning to tackle competitive influence maximization
K Ali, CY Wang, YS Chen - Knowledge and Information …, 2022 - search.proquest.com
Competitive influence maximization (CIM) is a key problem that seeks highly influential
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
Leveraging transfer learning in reinforcement learning to tackle competitive influence maximization.
K Ali, CY Wang, YS Chen - Knowledge & Information …, 2022 - search.ebscohost.com
Competitive influence maximization (CIM) is a key problem that seeks highly influential
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
Leveraging transfer learning in reinforcement learning to tackle competitive influence maximization
K Ali, CY Wang, YS Chen - Knowledge and Information Systems, 2022 - dl.acm.org
Competitive influence maximization (CIM) is a key problem that seeks highly influential
users to maximize the party's reward than the competitor. Heuristic and game theory-based …
users to maximize the party's reward than the competitor. Heuristic and game theory-based …