Junhao Wen
Biographic Data
| ID | 8245227 |
|---|---|
| NAME | Junhao Wen |
| GIVEN NAMES | Junhao |
| FAMILY NAME | Wen |
| SIGNATURE | WEN J |
| AFFILIATIONS | Chongqing University |
| ORCID | 0000-0003-2077-3070 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Frequency Meets Intent
Community-Enhanced Dynamic Graph Convolutional Networks for Rumor Detection on Social Networks
Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communitie…
Beyond Uniform Modeling
In real-world recommendation systems, user interactions contain behavioral information. Multibehavior sequential recommendation focuses on predicting the next item a user will interact with under a specific behavior. Incorporating behavioral information introduces diverse interaction patterns, which we treat as auxiliary signals to supplement trend interest modeling (e.g., primary interests, low-frequency interests, and noise). However, existing …
Is the emotional and cultural need satisfied or not? – A qualitative study based on youth users learning dance through Douyin
Social media has revolutionized information dissemination, including in dance education, yet there is a gap in understanding the emotional and cultural needs of youth users engaging with dance content on platforms like Douyin. This study uses use and gratification theory and constructivism to address this gap. Through semi-structured interviews with 12 youth users (aged 18–25, non-dance majors) of Douyin, the study identifies six key emotional an…
No prominent works on this page.
Community-Enhanced Dynamic Graph Convolutional Networks for Rumor Detection on Social Networks
Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communitie…
Beyond Uniform Modeling
In real-world recommendation systems, user interactions contain behavioral information. Multibehavior sequential recommendation focuses on predicting the next item a user will interact with under a specific behavior. Incorporating behavioral information introduces diverse interaction patterns, which we treat as auxiliary signals to supplement trend interest modeling (e.g., primary interests, low-frequency interests, and noise). However, existing …
Is the emotional and cultural need satisfied or not? – A qualitative study based on youth users learning dance through Douyin
Social media has revolutionized information dissemination, including in dance education, yet there is a gap in understanding the emotional and cultural needs of youth users engaging with dance content on platforms like Douyin. This study uses use and gratification theory and constructivism to address this gap. Through semi-structured interviews with 12 youth users (aged 18–25, non-dance majors) of Douyin, the study identifies six key emotional an…
Frequency Meets Intent
Recommender Systems and Techniques (2 works) · Advanced Bandit Algorithms Research (1 work) · Artificial Intelligence (1 work) · Behavioral modeling (1 work) · Complex Network Analysis Techniques (1 work) · Computer network (1 work) · Computer Science (1 work) · Computer security (1 work) · Contrastive analysis (1 work) · Cultural competence (1 work)