Information Diffusion Enhanced by Multi-Task Peer Prediction

• Published in: Journal of data intelligence Vol. 1; no. 1; pp. 18 - 35
• Main Authors: Ito, Kensuke, Ohsawa, Shohei, Tanaka, Hideyuki
• Format: Journal Article
• Language: English
• Published: 01.03.2020
• ISSN: 2577-610X; 2577-610X
• DOI: 10.26421/JDI1.1-2

Our study aims to strengthen truthfulness of the two-path mechanism: an information diffusion algorithm to find an influential node in non-cooperative directed acyclic graphs (DAGs). This subject is important because the two-path mechanism ensures only weak truthfulness (i.e., nodes are indifferent between reporting true or false out-edges), which restricts node selection accuracy. To enhance the mechanism, we employed an additional reward layer based on a multi-task peer prediction, where an informative equilibrium provides strictly higher rewards than any other equilibrium in virtually all cases (strong truthfulness). Rewards, which are derived from a comparison of each report, encourage a node to report true out-edges without affecting its own probability of being selected by the original two-path mechanism. We have also experimentally confirmed that our proposed {\em strongly truthful two-path mechanism} can sufficiently elicit true out-edges from each node.