Influence maximization based on dynamic personal perception in knowledge graph
Journal
Proceedings - International Conference on Data Engineering
Journal Volume
2021-April
Pages
1488-1499
Date Issued
2021
Author(s)
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 based on 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 at least 6 times of influence spread in large datasets over the state-of-the-art approaches. ? 2021 IEEE.
Subjects
Dynamic personal perceptions
Influence maximization
Item relationships
Multiple promotions
Approximation algorithms
Commerce
Large dataset
Influence maximizations
Knowledge graphs
Large datasets
Personal perception
Social influence
State-of-the-art approach
User perceptions
Viral marketing
Knowledge representation
SDGs
Type
conference paper