Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Author:

Zhang Bing1ORCID,Zhao Xuyang1ORCID,Nie Jiangtian2ORCID,Tang Jianhang3ORCID,Chen Yuling3ORCID,Zhang Yang4ORCID,Niyato Dusit5ORCID

Affiliation:

1. School of Information Science and Engineering, Yanshan University, Qin Huangdao, Hebei Province, China

2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

3. The State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China

4. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

5. College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore, Singapore

Abstract

Existing surveys and reviews on Influential Node Ranking Methods (INRMs) have primarily focused on technical details, neglecting thorough research on verifying the actual influence of these nodes in a network. This oversight may result in erroneous rankings. In this survey, we address this gap by conducting an extensive analysis of 82 primary studies related to INRMs based on the epidemic model over the past 20 years. We statistically analyze and categorize benchmark networks into four types, and conclude that high-quality networks with complete information are insufficient and most INRMs only focus on undirected and unweighted networks, which encourages collaboration between industry and academia to provide optimized networks. Additionally, we categorize and discuss the strengths, weaknesses, and optimized crafts of seven categories of INRMs, helping engineers and researchers narrow down their choices when selecting appropriate INRMs for their specific needs. Furthermore, we define the Capability and Correctness metrics and utilize their usage frequency and functionality to assist engineers and researchers in prioritizing and selecting suitable metrics for different INRMs and networks. To our knowledge, this is the first survey that systematically summarizes the Capability and Correctness of INRMs, providing insights for the complex network community and aiding INRM selection and evaluation.

Funder

S&T Program of Hebei

Natural Science Foundation of Hebei Province

Science Research Project of Hebei Education Department

National Research Foundation, Singapore

Infocomm Media Development Authority

Future Communications Research & Development Programme

DSO National Laboratories under the AI Singapore Programme

MOE Tier 1

National Natural Science Foundation of China

Foundation of State Key Laboratory of Public Big Data

National Key Research and Development Program of China

Guizhou Provincial Science and Technology Projects

Publisher

Association for Computing Machinery (ACM)

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