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.
SPADE: An Efficient Algorithm for Mining Frequent Sequences
Countries citing papers authored by Mohammed J. Zaki
Since
Specialization
Citations
This map shows the geographic impact of Mohammed J. Zaki'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 Mohammed J. Zaki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammed J. Zaki more than expected).
Fields of papers citing papers by Mohammed J. Zaki
This network shows the impact of papers produced by Mohammed J. Zaki. 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 Mohammed J. Zaki. The network helps show where Mohammed J. Zaki may publish in the future.
Co-authorship network of co-authors of Mohammed J. Zaki
This figure shows the co-authorship network connecting the top 25 collaborators of Mohammed J. Zaki.
A scholar is included among the top collaborators of Mohammed J. Zaki 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 Mohammed J. Zaki. Mohammed J. Zaki is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chen, Yu, Lingfei Wu, & Mohammed J. Zaki. (2020). Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. arXiv (Cornell University). 33. 19314–19326.9 indexed citations
5.
Saarela, Mirka, Bülent Yener, Mohammed J. Zaki, & Tommi Kärkkäinen. (2016). Predicting Math Performance from Raw Large-Scale Educational Assessments Data : A Machine Learning Approach. Jyväskylä University Digital Archive (University of Jyväskylä).14 indexed citations
6.
Zaki, Mohammed J., et al.. (2014). Parallel graph mining with GPUs. 1–16.7 indexed citations
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
9.
Chen, Jake Y., Mohammed J. Zaki, Gaurav Pandey, Huzefa Rangwala, & George Karypis. (2012). Proceedings of the 11th International Workshop on Data Mining in Bioinformatics. Knowledge Discovery and Data Mining.2 indexed citations
Nayak, Richi & Mohammed J. Zaki. (2006). Knowledge Discovery from XML Documents: First International Workshop, KDXD 2006, Singapore, April 9, 2006, Proceedings (Lecture Notes in Computer Science). Springer eBooks.2 indexed citations
12.
Goethals, Bart, Siegfried Nijssen, & Mohammed J. Zaki. (2005). Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)).3 indexed citations
Hu, Jingjing, et al.. (2002). Mining protein contact maps. 11(128). 3–10.41 indexed citations
15.
Zaki, Mohammed J. & Ching-Jui Hsiao. (2002). CHARM: An Efficient Algorithm for Closed Itemset Mining. 457–473.523 indexed citations breakdown →
16.
Krishnamoorthy, Mukkai S., et al.. (2001). LOGML - XML Language for Web Usage Mining.. Minerva Medica. 64(7). 320–2.2 indexed citations
17.
Zaki, Mohammed J.. (2000). Sequence mining in categorical domains: Algorithms and applications.1 indexed citations
18.
Zaki, Mohammed J., et al.. (1999). CHARM: An Efficient Algorithm for Closed Association Rule Mining.73 indexed citations
19.
Krishnamoorthy, Mukkai S., et al.. (1999). Clusterability Detection and Initial Seed Selection in Large Data Sets.3 indexed citations
20.
Zaki, Mohammed J., Neal Lesh, & Mitsunori Ogihara. (1998). PLANMINE: sequence mining for plan failures. Knowledge Discovery and Data Mining. 269–374.43 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.