Demo Data from VLDB 2026 / reviewers in the wild / expert
Minjian Liu
dblp:185/4320
· DBLP profile ↗
3ranked-venue papers
2as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 44% Data models and query languages · 44% Graph data management · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages › query language
declarative query language |
0.2 | 1 | 2016 | Rogas: A Declarative Framework for Network Analytics · Proc. VLDB Endow. 2016 |
Data mining
network analysis |
0.2 | 1 | 2016 | Rogas: A Declarative Framework for Network Analytics · Proc. VLDB Endow. 2016 |
Graph data management
graph query processing |
0.1 | 1 | 2016 | Rogas: A Declarative Framework for Network Analytics · Proc. VLDB Endow. 2016 |
Methods — techniques the papers use, named apart from their topics
declarative framework · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | DCGCD: Cognitive Diagnosis Based on Dual-Channel Graph Convolution Learning
Minjian Liu, Jinsen Ke, Qiuyan Zheng, Chonghui Yao |
IWCMC | 1 |
| 2016 | Data Analytics: From Conceptual Modelling to Logical Representation
Qing Wang 0002, Minjian Liu |
ER | 2 |
| 2016 | Rogas: A Declarative Framework for Network AnalyticsabstractNetwork analytics has become increasingly popular in recent years. Various graph systems have been developed for analysing networks, while network data is still largely stored and managed in relational database systems in the first place. As two separate systems are often used to manage and analyse network data, it not only increases the difficulty for users to learn and maintain these different systems simultaneously, but also impedes performing more sophisticated analysis on relational and topological properties of network data. Aiming to tackle these issues, we present Rogas in this paper, which is a declarative framework that allows the user to formulate analysis queries naturally without thinking about the tedious implementation details of graph algorithms and query processing. Minjian Liu, Qing Wang 0002 |
Proc. VLDB Endow. | 1 |