GraphHash: Topic-Aware Influence Maximization Using Hash

The influence maximization (IM) problem serve as a promising engine for the comprehension of social multimedia. However, current IM solutions often rely on fixed sampling and diffusion probabilities, which are insufficient to cater to the dynamic needs of online advertising, particularly in targeting users with diverse topic preferences. In addition, existing topic-aware IM solutions for online advertising are either time-consuming during the online query phase or burdened with substantial time and space overhead during the offline index construction phase. To solve the above problems, we propose GraphHash, a topic-aware target influence maximization solution that adopts a combination of offline and online phase. GraphHash constructs offline indexes by utilizing hash-based topic probabilities and selects the seed set with the maximal influence through intelligent indexing technique. In addition, we extend GraphHash using bottom-k sketching technology and apply it to different real-world graphs, achieving significant improvements of orders of magnitude over state-of-the-art baselines.