Pengfei Jiao
Biographic Data
| ID | 8350304 |
|---|---|
| NAME | Pengfei Jiao |
| GIVEN NAMES | Pengfei |
| FAMILY NAME | Jiao |
| SIGNATURE | JIAO P |
| AFFILIATIONS | Hangzhou Dianzi University |
| ORCID | 0000-0002-1972-9921 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Unified Network Embedding via Mutual Fusion of Communities and Roles
Most network embedding (NE) methods are either based on the proximity for community-guided tasks or on the structural similarity for role-oriented tasks. While being prevalent and effective, there still exists some potential issues that need further attention: 1) community and role are always regarded as orthogonal problems. They have rarely been combined to model the latent structures within the complex network. However, the generation of the ne…
Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks
Network embedding is an important class of link prediction methods, which can use the distance between learned low-dimensional node representations to characterize the similarity between nodes. Traditional network embedding methods focus on single-layer networks, while in reality, a large part of complex networks are not isolated, but interdependent and interrelated, forming multiplex complex networks. Also, how to effectively exploit layer corre…
Disentangled Representation Learning for Structural Role Discovery
Roles are defined as the equivalent classes of isomorphic nodes in the network. They focus on the local connective patterns and describe the structural similarities between nodes, and learning role-based network embeddings can help to recognize identities or functions of entities in real-world networks. This field has been studied over the past decades, however, the existing role-based network embedding methods all concentrate too much on disting…
Characteristic of 523 Covid-19 in Henan Province and a Death Prediction Model
Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in …
No prominent works on this page.
Characteristic of 523 Covid-19 in Henan Province and a Death Prediction Model
Certain high-risk factors related to the death of COVID-19 have been reported, however, there were few studies on a death prediction model. This study was conducted to delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) of different degree and establish a death prediction model. In this multi-centered, retrospective, observational study, we enrolled 523 COVID-19 cases discharged before February 20, 2020 in …
Inductive Link Prediction via Interactive Learning Across Relations in Multiplex Networks
Network embedding is an important class of link prediction methods, which can use the distance between learned low-dimensional node representations to characterize the similarity between nodes. Traditional network embedding methods focus on single-layer networks, while in reality, a large part of complex networks are not isolated, but interdependent and interrelated, forming multiplex complex networks. Also, how to effectively exploit layer corre…
Disentangled Representation Learning for Structural Role Discovery
Roles are defined as the equivalent classes of isomorphic nodes in the network. They focus on the local connective patterns and describe the structural similarities between nodes, and learning role-based network embeddings can help to recognize identities or functions of entities in real-world networks. This field has been studied over the past decades, however, the existing role-based network embedding methods all concentrate too much on disting…
Unified Network Embedding via Mutual Fusion of Communities and Roles
Most network embedding (NE) methods are either based on the proximity for community-guided tasks or on the structural similarity for role-oriented tasks. While being prevalent and effective, there still exists some potential issues that need further attention: 1) community and role are always regarded as orthogonal problems. They have rarely been combined to model the latent structures within the complex network. However, the generation of the ne…
Advanced Graph Neural Networks (2 works) · Artificial Intelligence (2 works) · Complex Network Analysis Techniques (2 works) · Computer Science (2 works) · Embedding (2 works) · Computer network (1 work) · COVID-19 and healthcare impacts (1 work) · COVID-19 Clinical Research Studies (1 work) · COVID-19 diagnosis using AI (1 work) · Data mining (1 work)