Incentive Compatible Mechanism for Influential Agent Selection

TL;DR

This paper introduces an incentive compatible mechanism for selecting influential agents in networks, ensuring truthful reporting and fairness, with provable bounds on the mechanism's effectiveness.

Contribution

It proposes the Geometric Mechanism for influence-based agent selection, ensuring incentive compatibility and fairness, and establishes an upper bound for such mechanisms.

Findings

The Geometric Mechanism achieves at least half of the optimal influence in expectation.

The mechanism guarantees incentive compatibility and fairness in agent selection.

An upper bound of 1/(1+ln 2) is proven for any incentive compatible and fair mechanism.

Abstract

Selecting the most influential agent in a network has huge practical value in applications. However, in many scenarios, the graph structure can only be known from agents' reports on their connections. In a self-interested setting, agents may strategically hide some connections to make themselves seem to be more important. In this paper, we study the incentive compatible (IC) selection mechanism to prevent such manipulations. Specifically, we model the progeny of an agent as her influence power, i.e., the number of nodes in the subgraph rooted at her. We then propose the Geometric Mechanism, which selects an agent with at least 1/2 of the optimal progeny in expectation under the properties of incentive compatibility and fairness. Fairness requires that two roots with the same contribution in two graphs are assigned the same probability. Furthermore, we prove an upper bound of 1/(1+\ln 2) for any incentive compatible and fair selection mechanisms.