Yunfei He
Biographic Data
| ID | 6930036 |
|---|---|
| NAME | Yunfei He |
| GIVEN NAMES | Yunfei |
| FAMILY NAME | He |
| SIGNATURE | HE Y |
| AFFILIATIONS | The Affiliated Yongchuan Hospital of Chongqing Medical University |
| ORCID | 0000-0001-8682-0293 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Label-Dependent Graph Neural Network
Graph neural network (GNN) provides a powerful expressive way to embed graph-structured data, which has been widely applied and spans many fields. This article found an interesting but unreasonable phenomenon in some classical GNNs, namely label confusion. Theoretically, the dependence between prediction label and ground-truth should gradually increase and tend to converge with training epochs. On the contrary, label confusion shows that the depe…
Equity and efficiency of health resource allocation in the Chengdu–Chongqing Economic Circle of China
Objective: This study aimed to evaluate the fairness and efficiency of health resource allocation (HRAE) in Chengdu-Chongqing Economic Circle after the new healthcare reform. This study also aimed to identify existing problems, providing empirical evidence for the government to formulate regional health plans scientifically and reasonably. Methods: The fairness of health resource allocation was analyzed using the Gini coefficient, Theil index, an…
Energy recovery and saving in municipal wastewater treatment engineering practices
Semantic Tradeoff for Heterogeneous Graph Embedding
Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, whic…
Semantic Tradeoff for Heterogeneous Graph Embedding
Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, whic…
Label-Dependent Graph Neural Network
Graph neural network (GNN) provides a powerful expressive way to embed graph-structured data, which has been widely applied and spans many fields. This article found an interesting but unreasonable phenomenon in some classical GNNs, namely label confusion. Theoretically, the dependence between prediction label and ground-truth should gradually increase and tend to converge with training epochs. On the contrary, label confusion shows that the depe…
Equity and efficiency of health resource allocation in the Chengdu–Chongqing Economic Circle of China
Objective: This study aimed to evaluate the fairness and efficiency of health resource allocation (HRAE) in Chengdu-Chongqing Economic Circle after the new healthcare reform. This study also aimed to identify existing problems, providing empirical evidence for the government to formulate regional health plans scientifically and reasonably. Methods: The fairness of health resource allocation was analyzed using the Gini coefficient, Theil index, an…
Energy recovery and saving in municipal wastewater treatment engineering practices
Computer Science (3 works) · Mathematics (3 works) · Advanced Graph Neural Networks (2 works) · Artificial Intelligence (2 works) · Complex Network Analysis Techniques (2 works) · Data mining (2 works) · Economics (2 works) · Graph (2 works) · Statistics (2 works) · Theoretical Computer Science (2 works)