A Multi-phase Approach for Improving Information Diffusion in Social Networks

TL;DR

This paper proposes a multi-phase influence maximization approach in social networks, formulating an objective function, analyzing properties, and developing algorithms for seed selection, budget-split, and delay to enhance influence spread.

Contribution

It introduces a novel multi-phase influence maximization framework with new algorithms and analysis for seed selection, budget-split, and timing under the independent cascade model.

Findings

Formulated a new objective function for two-phase influence maximization

Developed algorithms for seed node selection in multiple phases

Analyzed properties and optimal strategies for budget-split and delay

Abstract

For maximizing influence spread in a social network, given a certain budget on the number of seed nodes, we investigate the effects of selecting and activating the seed nodes in multiple phases. In particular, we formulate an appropriate objective function for two-phase influence maximization under the independent cascade model, investigate its properties, and propose algorithms for determining the seed nodes in the two phases. We also study the problem of determining an optimal budget-split and delay between the two phases.