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.
This map shows the geographic impact of Xiaofei He'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 Xiaofei He with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaofei He more than expected).
This network shows the impact of papers produced by Xiaofei He. 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 Xiaofei He. The network helps show where Xiaofei He may publish in the future.
Co-authorship network of co-authors of Xiaofei He
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaofei He.
A scholar is included among the top collaborators of Xiaofei He 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 Xiaofei He. Xiaofei He is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Weizhong, Bin Na Hong, Wei Liu, et al.. (2016). Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4016–4025.16 indexed citations
8.
Zhao, Zhou, Qifan Yang, Deng Cai, Xiaofei He, & Yueting Zhuang. (2016). Expert finding for community-based question answering via ranking metric network learning. International Joint Conference on Artificial Intelligence. 3000–3006.58 indexed citations
Zhang, Lijun, Jinfeng Yi, Rong Jin, Ming Lin, & Xiaofei He. (2013). Online Kernel Learning with a Near Optimal Sparsity Bound. International Conference on Machine Learning. 621–629.16 indexed citations
11.
Hu, Yao, Debing Zhang, Zhongming Jin, Deng Cai, & Xiaofei He. (2013). Active Learning Based on Local Representation.. International Joint Conference on Artificial Intelligence. 1415–1421.3 indexed citations
Tong, Xiaoyang, Shu Zhou, & Xiaofei He. (2010). An Agent Organization Theory and Its Application to Wide Area Backup Protection. Dianli xitong zidonghua. 34(13). 48–54.22 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.