Incentive-Compatible Diffusion

TL;DR

This paper introduces the 'Two Path' mechanism, an incentive-compatible method for influence maximization in directed graphs, particularly DAGs, balancing influence maximization with strategy-proofness and practical applicability.

Contribution

The paper proposes the 'Two Path' mechanism, a novel strategy-proof influence selection method for directed graphs, with proven incentive compatibility and approximation guarantees.

Findings

Incentive-compatible on DAGs

Finite approximation ratio on certain DAG families

Practical effectiveness shown in simulations

Abstract

Our work bridges the literature on incentive-compatible mechanism design and the literature on diffusion algorithms. We introduce the study of finding an incentive-compatible (strategy-proof) mechanism for selecting an influential vertex in a directed graph (e.g. Twitter's network). The goal is to devise a mechanism with a bounded ratio between the maximal influence and the influence of the selected user, and in which no user can improve its probability of being selected by following or unfollowing other users. We introduce the `Two Path' mechanism which is based on the idea of selecting the vertex that is the first intersection of two independent random walks in the network. The Two Path mechanism is incentive compatible on directed acyclic graphs (DAGs), and has a finite approximation ratio on natural subfamilies of DAGs. Simulations indicate that this mechanism is suitable for practical uses.