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.
Mining concept-drifting data streams using ensemble classifiers
2003783 citationsHaixun Wang, Wei Fan et al.profile →
Probase
2012491 citationsHongsong Li, Haixun Wang et al.profile →
Managing and Mining Graph Data
2010377 citationsCharų C. Aggarwal, Haixun Wangprofile →
BLINKS
2007342 citationsHaixun Wang, Philip S. Yu et al.profile →
Trinity
2013276 citationsBin Shao, Haixun Wang et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Haixun Wang'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 Haixun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haixun Wang more than expected).
This network shows the impact of papers produced by Haixun Wang. 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 Haixun Wang. The network helps show where Haixun Wang may publish in the future.
Co-authorship network of co-authors of Haixun Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Haixun Wang.
A scholar is included among the top collaborators of Haixun Wang 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 Haixun Wang. Haixun Wang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Zhongyuan & Haixun Wang. (2016). Understanding Short Texts. Meeting of the Association for Computational Linguistics.13 indexed citations
5.
Xiao, Yanghua, et al.. (2015). On conceptual labeling of a bag of words. International Conference on Artificial Intelligence. 1326–1332.8 indexed citations
6.
Song, Yangqiu, Shusen Wang, & Haixun Wang. (2015). Open domain short text conceptualization: a generative + descriptive modeling approach. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 3820–3826.16 indexed citations
7.
Kim, Dongwoo, Haixun Wang, & Alice Oh. (2013). Context-dependent conceptualization. ANU Open Research (Australian National University). 2654–2661.34 indexed citations
8.
Chen, Xuewen, Guy Lebanon, Haixun Wang, & Mohammed J. Zaki. (2012). Proceedings of the 21st ACM international conference on Information and knowledge management.13 indexed citations
Cambria, Erik, et al.. (2011). A Common and Common Sense Knowledge Base for Opinion Mining.1 indexed citations
12.
Xie, Min, Haixun Wang, Jian Yin, & Xiaofeng Meng. (2007). Integrity auditing of outsourced data. Very Large Data Bases. 782–793.98 indexed citations
13.
Wu, Kun‐Lung, Kirsten Hildrum, Wei Fan, et al.. (2007). Challenges and experience in prototyping a multi-modal stream analytic and monitoring application on System S. Very Large Data Bases. 1185–1196.61 indexed citations
14.
Lim, Lipyeow, Haixun Wang, & Min Wang. (2007). Unifying data and domain knowledge using virtual views. Very Large Data Bases. 255–266.8 indexed citations
Fan, Wei, Haixun Wang, Philip S. Yu, & Shaw‐Hwa Lo. (2003). Inductive learning in less than one sequential data scan. International Joint Conference on Artificial Intelligence. 595–600.1 indexed citations
Wang, Haixun & Carlo Zaniolo. (2000). Using SQL to Build New Aggregates and Extenders for Object- Relational Systems. Very Large Data Bases. 166–175.31 indexed citations
19.
Wang, Haixun & Carlo Zaniolo. (2000). Database System Extensions for Decision Support: the AXL Approach.. International Conference on Management of Data. 11–20.4 indexed citations
20.
Wang, Haixun & Carlo Zaniolo. (1999). User-Defined Aggregates for Datamining.. International Conference on Management of Data.3 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.