Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph

TL;DR

This paper introduces a novel influence maximization approach that incorporates dynamic user perception via knowledge graphs, significantly improving influence spread in social networks.

Contribution

It formulates the IMDPP problem considering dynamic perception, proves its hardness, and proposes Dysim, an approximation algorithm leveraging knowledge graphs for better influence maximization.

Findings

Dysim outperforms state-of-the-art methods by up to 6.7 times in influence spread.

The approach effectively captures dynamic user preferences using knowledge graphs.

Experimental results validate the efficiency and effectiveness of Dysim in real social networks.

Abstract

Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to capture dynamic user perception, we formulate the problem of Influence Maximization with Dynamic Personal Perception (IMDPP) that considers user preferences and social influence reflecting the impact of relevant item adoptions. We prove the hardness of IMDPP and design an approximation algorithm, named Dynamic perception for seeding in target markets (Dysim), by exploring the concepts of dynamic reachability, target markets, and substantial influence to select and promote a sequence of relevant items. We evaluate the performance of Dysim in comparison with the state-of-the-art approaches using real social networks with real KGs. The experimental results show that Dysim effectively achieves up to 6.7 times of influence spread in large datasets over the state-of-the-art approaches.