Qianmu Li
Biographic Data
| ID | 4470144 |
|---|---|
| NAME | Qianmu Li |
| GIVEN NAMES | Qianmu |
| FAMILY NAME | Li |
| SIGNATURE | LI Q |
| AFFILIATIONS | Nanjing University of Science and Technology |
| ORCID | 0000-0002-0998-1517 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Vats
Detecting multimodal deepfakes has become a pressing concern due to the rising sophistication of generative techniques capable of creating highly convincing visual-speech synchronized deepfakes. As these manipulated contents proliferate on social media, remote education, smart meetings, smart homes, and virtual reality (VR), their increasing realism significantly impacts individuals, underscoring the urgent need for effective detection methods to…
Influence Maximization via Hyperbolic Deep Graph Learning in Cyber-Physical-Social Systems
Influence maximization in cyber-physical social systems has an important application background in the field of viral marketing. It attracts extensive research in academic and industrial communities. The state-of-the-art influence maximization algorithms estimate the influence of users on the sampled sub-networks. However, with the explosive growth in data resources of cyber-physical social systems, the generation of these samples becomes expensi…
Evolution of smart grid cybersecurity
FDGNN
Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item inte…
No prominent works on this page.
FDGNN
Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item inte…
Influence Maximization via Hyperbolic Deep Graph Learning in Cyber-Physical-Social Systems
Influence maximization in cyber-physical social systems has an important application background in the field of viral marketing. It attracts extensive research in academic and industrial communities. The state-of-the-art influence maximization algorithms estimate the influence of users on the sampled sub-networks. However, with the explosive growth in data resources of cyber-physical social systems, the generation of these samples becomes expensi…
Evolution of smart grid cybersecurity
Vats
Detecting multimodal deepfakes has become a pressing concern due to the rising sophistication of generative techniques capable of creating highly convincing visual-speech synchronized deepfakes. As these manipulated contents proliferate on social media, remote education, smart meetings, smart homes, and virtual reality (VR), their increasing realism significantly impacts individuals, underscoring the urgent need for effective detection methods to…
Advanced Graph Neural Networks (2 works) · Graph (2 works) · Mental Health via Writing (2 works) · Artificial Intelligence (1 works) · Bipartite graph (1 works) · Blockchain Technology Applications and Security (1 works) · Complex Network Analysis Techniques (1 works) · Computer Science (1 works) · Data mining (1 works) · Digital Media Forensic Detection (1 works)