Seeds Buffering for Information Spreading Processes

TL;DR

This paper introduces a buffering strategy for sequential seeding in social influence processes, improving influence spread efficiency by better resource allocation and dynamic node selection.

Contribution

It proposes a novel buffering extension to sequential seeding, enhancing influence maximization by avoiding redundant activations and optimizing seed node selection.

Findings

Buffer-based sequential seeding outperforms traditional methods in coverage and speed.

Dynamic rankings and network area detection improve influence spread.

Method is effective on both real and artificial social networks.

Abstract

Seeding strategies for influence maximization in social networks have been studied for more than a decade. They have mainly relied on the activation of all resources (seeds) simultaneously in the beginning; yet, it has been shown that sequential seeding strategies are commonly better. This research focuses on studying sequential seeding with buffering, which is an extension to basic sequential seeding concept. The proposed method avoids choosing nodes that will be activated through the natural diffusion process, which is leading to better use of the budget for activating seed nodes in the social influence process. This approach was compared with sequential seeding without buffering and single stage seeding. The results on both real and artificial social networks confirm that the buffer-based consecutive seeding is a good trade-off between the final coverage and the time to reach it. It performs significantly better than its rivals for a fixed budget. The gain is obtained by dynamic rankings and the ability to detect network areas with nodes that are not yet activated and have high potential of activating their neighbours.