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Zongli Liu

dblp:248/4503 · DBLP profile ↗
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6ranked-venue papers
1as first author
4since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-author · 3 since 2021Security and privacy · 2Databases, data management, data science and information retrieval · 1 · 1 since 2021

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.

Artificial intelligence
1 paper
Graph learning · 67% Probabilistic and Bayesian machine learning · 33%
Databases, data mining, and information retrieval
1 paper
Machine learning and data management · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning › efficient graph learning › data-efficient graph learning
active learning on graphs
1.012026
Hierarchical Cluster-based Open-World Graph Active Learning · SIGIR 2026
Machine learning › Probabilistic and Bayesian machine learning › clustering
hierarchical clustering
1.012026
Hierarchical Cluster-based Open-World Graph Active Learning · SIGIR 2026
Machine learning › Graph learning
open-world graph learning
1.012026
Hierarchical Cluster-based Open-World Graph Active Learning · SIGIR 2026

Methods — techniques the papers use, named apart from their topics

hierarchical clustering · 2.0active learning · 2.0
YearPublicationVenuePosition
2026 Hierarchical Cluster-based Open-World Graph Active Learning
Yayong Li, Zhengyi Du, Jonathan Wilton, Jinran Wu, Zongli Liu
SIGIR6
2025 Generalized Few-Shot Node Classification via Training Set Refinement
Yayong Li, Xubo Zhang, Zongli Liu, Jinran Wu
PRICAI5
2025 A multi-task optimization algorithm via reinforcement learning for multimodal multi-objective optimization
Jie Cao 0014, Yuze Yang, Jianlin Zhang 0002, Zuohan Chen, Zongli Liu
Expert Syst. Appl.5
2024 A constrained multi-objective evolutionary algorithm with Pareto estimation via neural network
Zongli Liu, Jie Cao 0014, Jianlin Zhang 0002, Zuohan Chen
Expert Syst. Appl.1
2019 Information Hiding in OOXML Format Data based on the Splitting of Text Elements
abstract
In this paper, a novel information hiding method is proposed to embed data in the Word documents that use OOXML format. The 2007 version and more recent versions of MS Word are all based on the OOXML format. The main document body of OOXML document consists of the text elements that correspond to the content of the document. It is found that, in the OOXML format, the printable text in an element can be “split” by separating the element into multiple elements. The digital code of the information determines if the adjacent characters in the text will be “split” or not, so as to achieve the purpose of information hiding. Since the format and all the other properties of the text in the OOXML format document is unchanged after the embedding, the embedded information is imperceptible. The code of the information is embedded circularly to enhance robustness. Experiments show that the proposed method can deal with many kinds of attacks, including “content”, “save as”, “copy” and part of “format”. The proposed method can be used in information security and copyright protection for OOXML format documents.
Wenjie Guo, Yonggang Lu, Yi Yang 0017, Lian Li 0003, Zongli Liu
ISI6
2019 Text Watermarking for OOXML Format Documents Based on Color Transformation
abstract
A robust text watermarking approach has been proposed in this paper, in which the watermarks are embedded into the text by the transformation of characters' RGB-style color in the OOXML format document. To get high watermark embedding capacity, two bits of the watermarking information are embedded between every two characters. The initial text content of the document is not changed after the embedding because of the good characteristics of OOXML format and the subtle adjustment of characters' color, which ensure the good imperceptibility of the watermarking method. Most attacks such as “copy”, “save as”, “insert” and “delete” operations can be resisted by the proposed approach, and the location of the “insert” and “delete” operations can be detected by redundant embedding of the watermarking information. The proposed approach can be applied to files in “docx” and “doc” format.
Wenjie Guo, Yonggang Lu, Yi Yang 0017, Lian Li 0003, Zongli Liu
ISI6