Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Learning Bayesian networks from data: An information-theory based approach
2002502 citationsDavid Bell, Weiru Liu et al.profile →
This map shows the geographic impact of Weiru Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Weiru Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Weiru Liu more than expected).
This network shows the impact of papers produced by Weiru Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Weiru Liu. The network helps show where Weiru Liu may publish in the future.
Co-authorship network of co-authors of Weiru Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Weiru Liu.
A scholar is included among the top collaborators of Weiru Liu based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Weiru Liu. Weiru Liu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Weiru, et al.. (2017). Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS2017). Adaptive Agents and Multi-Agents Systems.71 indexed citations
Liu, Weiru, et al.. (2014). CAN(PLAN)+: extending the operational semantics of the BDI architecture to deal with uncertain information. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 52–61.13 indexed citations
12.
Liu, Weiru, et al.. (2013). Scalable Uncertainty Management - 7th International Conference, SUM 2013. Research Portal (Queen's University Belfast).7 indexed citations
13.
Ma, Wenjun, Xudong Luo, & Weiru Liu. (2013). An ambiguity aversion framework of security games under ambiguities. International Joint Conference on Artificial Intelligence. 271–278.8 indexed citations
Liu, Weiru, et al.. (2009). A syntax-based framework for merging imprecise probabilistic logic programs. Research Portal (Queen's University Belfast). 1990–1995.3 indexed citations
16.
Liu, Weiru, et al.. (2008). Revising imprecise probabilistic beliefs in the framework of probabilistic logic programming. Research Portal (Queen's University Belfast). 590–596.4 indexed citations
17.
Qi, Guilin, Weiru Liu, & David Bell. (2006). Merging stratified knowledge bases under constraints. National Conference on Artificial Intelligence. 281–286.13 indexed citations
18.
Qi, Guilin, Weiru Liu, & David Bell. (2005). Measuring conflict and agreement between two prioritized belief bases. Research Portal (Queen's University Belfast). 552–557.16 indexed citations
Qi, Guilin, Weiru Liu, & David H. Glass. (2004). A split-combination method for merging inconsistent possibilistic knowledge bases. Research Portal (Queen's University Belfast). 348–356.8 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.