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Pengfei Wan 0002

dblp:119/0306-2 · DBLP profile ↗
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3ranked-venue papers
2as first author
3since 2021 · last 2023
0000-0001-9281-1529ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Mathematical optimization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities › social network analysis
information diffusion
0.512021
Intervening Coupling Diffusion of Competitive Information in Online Social Networks · IEEE Trans. Knowl. Data Eng. 2021
Mathematical optimization › control theory › optimal control
constrained optimal control
0.512021
Intervening Coupling Diffusion of Competitive Information in Online Social Networks · IEEE Trans. Knowl. Data Eng. 2021
Mathematical optimization › control theory
optimal control
0.512021
Intervening Coupling Diffusion of Competitive Information in Online Social Networks · IEEE Trans. Knowl. Data Eng. 2021

Methods — techniques the papers use, named apart from their topics

optimal control theory · 1.0epidemic diffusion modeling · 1.0
YearPublicationVenuePosition
2023 A novel rumor detection with multi-objective loss functions in online social networks
Pengfei Wan 0002, Xiaoming Wang 0001, Guangyao Pang, Liang Wang 0014, Geyong Min
Expert Syst. Appl.1
2021 Dual Attention Network Based on Knowledge Graph for News Recommendation
Xiaoming Wang 0001, Guangyao Pang, Yaguang Lin, Pengfei Wan 0002
WASA (1)5
2021 Intervening Coupling Diffusion of Competitive Information in Online Social Networks
abstract
The vigorously rising of social media brings a new opportunity for information diffusion in online social networks. However, the existing models of information diffusion only consider the single information, such as rumor. What's more, most of intervention frameworks are modeled under the ideal circumstances without reality constraints. In this article, we propose a novel model of competitive information coupling diffusion to describe the complex process of information diffusion in online social networks. Especially, in order to intervene the process of competitive information coupling diffusion, we introduce three intervention strategies and propose an intervention framework. More importantly, we take the dynamic constraints into consideration such as the budget of intervention and current state of the system, and further propose the constrained intervention model. To reduce the system loss, we establish an optimal control problem with constraints to achieve the optimal allocation of intervention strategies over time and minimize the total loss. We theoretically prove the existence and uniqueness of the optimal solution of the problem, and derive the optimal control solution. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of different intervention strategies about competitive information coupling diffusion with or without constraints, respectively. The results show that the collaborative intervention strategies can effectively impact the process of diffusion and get the minimum system loss. This article provides high realistic significance to the commercial marketing in online social networks.
Pengfei Wan 0002, Xiaoming Wang 0001, Xinyan Wang 0001, Liang Wang 0014, Yaguang Lin, Wei Zhao 0001
IEEE Trans. Knowl. Data Eng.1