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 on data center cooling systems: Technology, power consumption modeling and control strategy optimization
This map shows the geographic impact of Junyu Niu'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 Junyu Niu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junyu Niu more than expected).
This network shows the impact of papers produced by Junyu Niu. 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 Junyu Niu. The network helps show where Junyu Niu may publish in the future.
Co-authorship network of co-authors of Junyu Niu
This figure shows the co-authorship network connecting the top 25 collaborators of Junyu Niu.
A scholar is included among the top collaborators of Junyu Niu 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 Junyu Niu. Junyu Niu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sha, Chaofeng, Xiaowei Wu, & Junyu Niu. (2016). A framework for recommending relevant and diverse items. International Joint Conference on Artificial Intelligence. 3868–3874.27 indexed citations
Yao, Jing, Jun Xu, & Junyu Niu. (2008). Using Role Determination and Expert Mining in the Enterprise Environment.. Text REtrieval Conference.3 indexed citations
10.
Niu, Junyu. (2007). A Closeness-Based Semi-Supervised Text Classification Method. Zhongwen xinxi xuebao.1 indexed citations
11.
Niu, Junyu. (2007). Feature Selection Method Based on Domain-specific Term Extraction. Journal of Chinese Computer Systems.2 indexed citations
12.
Niu, Junyu. (2007). Research of Language Model in Information Retrieval. Jisuanji gongcheng.2 indexed citations
13.
Xu, Jun, et al.. (2007). WIM at TREC 2007. Text REtrieval Conference.1 indexed citations
Lin, Chen & Junyu Niu. (2006). Judging Expertise-WIM at Enterprise. Text REtrieval Conference.2 indexed citations
17.
Zheng, Haiqing, et al.. (2006). Using Profile Matching and Text Categorization for Answer Extraction in TREC Genomics.. Text REtrieval Conference.1 indexed citations
18.
Niu, Junyu, et al.. (2005). WIM at TREC 2005.. Text REtrieval Conference.3 indexed citations
19.
Wu, Lide, Jimmy Xiangji Huang, Junyu Niu, et al.. (2002). FDU at TREC 2002: Filtering, Q&A, Web and Video Tasks.. Text REtrieval Conference.15 indexed citations
20.
Wu, Lide, Jimmy Xiangji Huang, Junyu Niu, et al.. (2001). FDU at TREC-10: Filtering, QA, Web and Video Tasks. Text REtrieval Conference. 192–207.5 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.