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Minjian Liu

dblp:185/4320 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Data models and query languages › query language
declarative query language
0.212016
Rogas: A Declarative Framework for Network Analytics · Proc. VLDB Endow. 2016
Data mining
network analysis
0.212016
Rogas: A Declarative Framework for Network Analytics · Proc. VLDB Endow. 2016
Graph data management
graph query processing
0.112016
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
YearPublicationVenuePosition
2026 DCGCD: Cognitive Diagnosis Based on Dual-Channel Graph Convolution Learning
Minjian Liu, Jinsen Ke, Qiuyan Zheng, Chonghui Yao
IWCMC1
2016 Data Analytics: From Conceptual Modelling to Logical Representation
Qing Wang 0002, Minjian Liu
ER2
2016 Rogas: A Declarative Framework for Network Analytics
abstract
Network 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