Influence Maximization with Spontaneous User Adoption

Lichao Sun, Albert Chen, Philip S. Yu, Wei Chen
2020 Proceedings of the 13th International Conference on Web Search and Data Mining  
We incorporate the realistic scenario of spontaneous user adoption into influence propagation (also refer to as self-activation) and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes. Self activation occurs in many real world situations; for example, people naturally share product recommendations with their friends, even without marketing
more » ... n. Under the SAIC model, we study three influence maximization problems: (a) boosted influence maximization (BIM) aims to maximize the total influence spread from both self-activated nodes and selected seeds; (b) preemptive influence maximization (PIM) aims to find nodes that, if self-activated, can reach the most number of nodes before other self-activated nodes; and (c) boosted preemptive influence maximization (BPIM) aims to select seeds that are guaranteed to be activated and can reach the most number of nodes before other self-activated nodes. We propose scalable algorithms for all three problems and prove that they achieve 1 − 1/ − approximation for BIM and BPIM and 1 − for PIM, for any > 0. Through extensive tests on realworld graphs, we demonstrate that our algorithms outperform the baseline algorithms significantly for the PIM problem in solution quality, and also outperform the baselines for BIM and BPIM when self-activation behaviors are nonuniform across nodes.
doi:10.1145/3336191.3371791 dblp:conf/wsdm/SunCYC20 fatcat:pme3eq3amndl3epyl52pm3fqya