EDBT 2026 Demo / reviewers in the wild / expert
Chengjie Mao
dblp:05/560
· DBLP profile ↗
7ranked-venue papers
0as first author
4since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 4 · 4 since 2021Human-computer interaction and ubiquitous computing · 3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | HGNNIM: A Hypergraph Neural Network-Based Approach to Maximize Influence in Social Networks
Runbin Yao, Wenli Fang, Chao Chang 0002, Luyao Teng, Chengzhe Yuan, Hao Zhong 0007, Chengjie Mao |
WISA | 7 |
| 2024 | Attribute Multiplex Network Graph Clustering: Joint Contrastive And High-Order Proximity
Xijia Lin, Chengjie Mao |
WISA | 5 |
| 2023 | Informative Anchor-Enhanced Heterogeneous Global Graph Neural Networks for Personalized Session-Based Recommendation
Ronghua Lin, Luyao Teng, Feiyi Tang, Hao Zhong 0007, Chengzhe Yuan, Chengjie Mao |
WISE | 6 |
| 2023 | Prompt-Learning for Semi-supervised Text Classification
Chengzhe Yuan, Zekai Zhou, Feiyi Tang, Ronghua Lin, Chengjie Mao, Luyao Teng |
WISE | 5 |
| 2014 | A new team recommendation model with applications in social networkabstractNowadays, some users with similar interests or same tasks may form a cooperative team in the social network, where resources and information can be wildly shared. With more and more users joining in the social network, the number of teams is growing up at the same time. How to recommend a team in which a user is interested has become a new and important topic in the social network. The purpose of this paper is to propose a novel model for team recommendation to help users find interesting teams in social network ,which is using collaborative filtering algorithm and trust propagation theory. Formally, we construct team recommendation lists from three steps. Firstly, it computes the team similarity behavior between the user and his/her friends and get the first recommendation team list from it. Secondly, it gets the team recommendation list from the user's latent friends list. Finally, the third list is got from the hot teams by applied weighting factor theory. We obtain the data from a social network called schol@t (www.scholat.com) to make experimental analysis, which demonstrate that the team recommendation model is effective. Shaowen Hong, Chengjie Mao, Zhenxiong Yang, Hanjiang Lai |
CSCWD | 2 |
| 2001 | The Control and Algorithm of Audio Dynamic BufferabstractIt is a very important work to improve the quality of voice on transferred Internet. In this paper two aspects about voice quality are analyzed, and an algorithm of dynamic setting audio buffer for improving QoS is proposed. Finally the simulation results of the algorithms and the conclusion are given. Chongjia Peng, Yong Tang 0001, Xianji Li, Chengjie Mao |
CSCWD | 4 |
| 2001 | Research on Adaptive IP QoS Management FrameworkabstractIP QoS is one of the core technologies of IP network. The mechanisms to achieve IP QoS could be sub bandwidth management (SBM), multiple protocols label switching (MPLS), IntServ, DiffServ and so on. But some of the above should be integrated for implementing completed IP QoS. At present, one of the most popular ideas is IntServ on the edge of network and DiffServ on the backbone. In this paper, we present a framework for completed end-to-end adaptive IP QoS by integrating DiffServ and IntServ, put forward the negotiation flowchart of adaptive QoS on the boundary router and the QoS control flow-chart on the Interior router. Yong Tang 0001, Chengjie Mao, Hai-xiang Ou, Xue-liang Yang, Xianji Li |
CSCWD | 2 |