Voting-based Opinion Maximization A summary of the IEEE International Conference on Data Engineering (ICDE) 2023 research paper by Arkaprava Saha, Xiangyu Ke, Arijit Khan, and Laks V. S. Lakshmanan Background: We investigate the novel problem of voting-based opinion maximization in a social network. In the presence of competing campaigns, we find a given number of seed nodes for a target campaigner that maximize a voting-based score for the target campaigner at a given time-horizon. Our work bridges two different paradigms: (1) seed selection for opinion formation and diffusion till a given finite time-horizon, and (2) voting-based winning criteria (e.g., plurality, Copeland) with multiple campaigners. The classic influence maximization (IM) problem [1, 2] identifies the top-k seed users in a social network to maximize the expected number of influenced users in the network, starting from those seed nodes and following an influence diffusion model, e.g., independent cascade (IC