IPIM: An Effective Contribution-Driven Information Propagation Incentive Mechanism

The wide diffusion of information in social networks can be exploited to solve searching-for-a-target (SFT) problems including those of missing individuals. Incentive mechanisms that promote active individual participation can be designed to favor a clear propagation direction to help efficiently find a target. However, the existing incentive research rarely focuses on a clear propagation direction based on a specific goal. Thus, we propose an effective contribution-driven information propagation incentive mechanism (IPIM) that exploits ego networks to solve the SFT problem. First, we use an all-pay auction-inspired model to determine the propagation of alters in each ego network. We then propose a novel algorithm, the node propagation utility, based on effective contributions, to focus the propagation toward the target rather than searching indiscriminately and inefficiently. The theoretical analyses and simulation results indicate that IPIM guarantees the truthfulness, individual rationality, and budget feasibility. The simulations are conducted based on real and public social datasets. The IPIM shows increased efficiencies of 951.18 % of success rate, of 215.65 % in propagation hops, and of 514.41 % in participation scale, compared with a typical incentive mechanism. In conclusion, the IPIM shows significant value in the potential application in SFT.