Efficient Approximation Algorithms for Adaptive Target Profit Maximization

TL;DR

This paper introduces new adaptive algorithms for target profit maximization in social networks, improving influence-based revenue maximization through multiple seed selection batches with theoretical guarantees and experimental validation.

Contribution

It proposes novel adaptive algorithms ADG, AddATP, and HATP for target profit maximization under different models, with strong theoretical analysis and enhanced efficiency.

Findings

Algorithms outperform existing methods in experiments

HATP significantly boosts efficiency under noise model

Theoretical guarantees validate algorithm effectiveness

Abstract

Given a social network $G$, the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed selection. The target profit maximization (TPM) problem, which generalizes the PM problem, aims to select a subset of seed nodes from a target user set $T$ to maximize the profit. Existing algorithms for PM mostly consider the nonadaptive setting, where all seed nodes are selected in one batch without any knowledge on how they may influence other users. In this paper, we study TPM in adaptive setting, where the seed users are selected through multiple batches, such that the selection of a batch exploits the knowledge of actual influence in the previous batches. To acquire an overall understanding, we study the adaptive TPM problem under both the oracle model and the noise model, and propose ADG and AddATP algorithms to address them with strong theoretical guarantees, respectively. In addition, to better handle the sampling errors under the noise model, we propose the idea of hybrid error based on which we design a novel algorithm HATP that boosts the efficiency of AddATP significantly. We conduct extensive experiments on real social networks to evaluate the performance, and the experimental results strongly confirm the superiorities and effectiveness of our solutions.