Demo Data from EDBT 2026 / reviewers in the wild / expert
Hengming Wang
dblp:346/2691
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 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.
| Network and information security
1 paper |
Privacy and data protection · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Web and social media mining · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Web and social media mining
information diffusion |
0.7 | 1 | 2023 | Privacy Data Diffusion Modeling and Preserving in Online Social Network · IEEE Trans. Knowl. Data Eng. 2023 |
Privacy and data protection
privacy information diffusion |
0.7 | 1 | 2023 | Privacy Data Diffusion Modeling and Preserving in Online Social Network · IEEE Trans. Knowl. Data Eng. 2023 |
Privacy and data protection
social network privacy |
0.7 | 1 | 2023 | Privacy Data Diffusion Modeling and Preserving in Online Social Network · IEEE Trans. Knowl. Data Eng. 2023 |
Methods — techniques the papers use, named apart from their topics
complex network analysis · 1.3block mechanism · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Privacy Data Diffusion Modeling and Preserving in Online Social NetworkabstractWith the ubiquity of social media, privacy leakage has become a urgent problemfor social media managers. Studying how the privacy information diffuses through social media has attracted much attention. As a prerequisite, modeling privacy information diffusion is important research. Current approaches for modeling information diffusion are not available for privacy information since they did not consider the propagation features of privacy information in social media. Thispaper discusses the problem of modeling privacy information in social media and its challenges. We first analyse the information diffusion paths in the basic parameters of complex network and the high-order structures. We find that the privacy information is different in propagation features and the size of star structures. Second, a new information diffusion model is illustrated to simulate the diffusion process of information in social media by considering the following three parameters: 1) the probability of users receiving this message, 2) the probability that users have a tendency to forward this message and 3) the interest the users hold for this message. Finally, a block mechanism is designed to congest the diffusion of privacy information in social media. Xiangyu Hu 0006, Tianqing Zhu, Xuemeng Zhai, Hengming Wang, Wanlei Zhou 0001, Wei Zhao 0001 |
IEEE Trans. Knowl. Data Eng. | 4 |