IPIM: An Effective Contribution-Driven Information Propagation Incentive Mechanism
2019
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
propagationdirection to help efficiently find a target. However, the existing incentive research rarely focuses on a clear
propagationdirection based on a specific goal. Thus, we propose an effective contribution-driven information
propagationincentive mechanism (IPIM) that exploits ego networks to solve the SFT problem. First, we use an
all-pay auction-inspired model to determine the
propagationof alters in each ego network. We then propose a novel algorithm, the node
propagationutility, based on effective contributions, to focus the
propagationtoward 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
propagationhops, 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.
Keywords:
-
Correction
-
Source
-
Cite
-
Save
0
References
1
Citations
NaN
KQI