Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors in Geo-Social Networks

TL;DR

This paper introduces a framework for selecting optimal points of interest in geo-social networks to maximize influence, addressing computational challenges with efficient algorithms and indexes, and demonstrating effectiveness through extensive experiments.

Contribution

It proposes a novel framework with indexes and algorithms for influence maximization in geo-social networks, handling NP-hardness and providing practical solutions.

Findings

Framework achieves efficient influence maximization.

Algorithms perform well on real and synthetic data.

Provides approximate and heuristic solutions for complex problems.

Abstract

Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting, we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBRkNN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially attractive to social influencers who are expected to largely promote the POIs through online influence propagation. In other words, the problem aims to detect an optimal set of POIs with the largest word-of-mouth (WOM) marketing potential. This functionality is useful in various real-life applications, including social advertising, location-based viral marketing, and personalized POI recommendation. However, solving MaxInfBRkNN with theoretical guarantees is challenging, because of the prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and #P-hardness in finding the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efficient batch BRkNN processing algorithms, and alternative POI selection policies that support both approximate and heuristic solutions. Extensive experiments on real and synthetic datasets demonstrate the good performance of our proposed methods.