1 research outputs found
Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph
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