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.
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture
This map shows the geographic impact of Qiang 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 Qiang Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qiang Liu more than expected).
This network shows the impact of papers produced by Qiang 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 Qiang Liu. The network helps show where Qiang Liu may publish in the future.
Co-authorship network of co-authors of Qiang Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Qiang Liu.
A scholar is included among the top collaborators of Qiang 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 Qiang Liu. Qiang Liu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ye, Mao, et al.. (2020). Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. International Conference on Machine Learning. 10820–10830.6 indexed citations
Han, Jun & Qiang Liu. (2017). Stein Variational Adaptive Importance Sampling.. Uncertainty in Artificial Intelligence.2 indexed citations
15.
Hu, Xiping, Jun Cheng, Xitong Li, et al.. (2017). Mobile Cyber-Physical System. Mobile Information Systems. 2017. 1–2.3 indexed citations
16.
Zhu, Yanhui, et al.. (2017). A Novel Image Segmentation Method Based on An Improved Bacterial Foraging Optimization Algorithm.. J. Inf. Hiding Multim. Signal Process.. 8. 348–357.2 indexed citations
17.
Zhou, Dengyong, Qiang Liu, John Platt, & Christopher Meek. (2014). Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. International Conference on Machine Learning. 262–270.48 indexed citations
18.
Asuncion, Arthur, Qiang Liu, Alexander Ihler, & Padhraic Smyth. (2010). Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. International Conference on Machine Learning. 47–54.11 indexed citations
19.
Asuncion, Arthur, Qiang Liu, Alexander Ihler, & Padhraic Smyth. (2010). Learning with Blocks: Composite Likelihood and Contrastive Divergence. International Conference on Artificial Intelligence and Statistics. 33–40.24 indexed citations
20.
Liu, Qiang. (2005). NEW CHANGE DETECTION MODELS FOR OBJECT-BASED ENCODING OF PATIENT MONITORING VIDEO. D-Scholarship@Pitt (University of Pittsburgh). 29(6). 439–444.2 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.