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 Stan Matwin'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 Stan Matwin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stan Matwin more than expected).
This network shows the impact of papers produced by Stan Matwin. 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 Stan Matwin. The network helps show where Stan Matwin may publish in the future.
Co-authorship network of co-authors of Stan Matwin
This figure shows the co-authorship network connecting the top 25 collaborators of Stan Matwin.
A scholar is included among the top collaborators of Stan Matwin 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 Stan Matwin. Stan Matwin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Matwin, Stan, et al.. (2019). Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities. Meeting of the Association for Computational Linguistics. 46–48.1 indexed citations
Liu, Bo, et al.. (2015). Ship movement anomaly detection using specialized distance measures. International Conference on Information Fusion. 1113–1120.14 indexed citations
8.
Matwin, Stan, et al.. (2014). Combining Binary Classifiers for a Multiclass Problem with Differential Privacy. 7(1). 51–70.1 indexed citations
9.
Sokolova, Marina, et al.. (2013). Authorship Attribution in Health Forums. Recent Advances in Natural Language Processing. 74–82.3 indexed citations
10.
Kiritchenko, Svetlana & Stan Matwin. (2011). Email classification with co-training. Conference of the Centre for Advanced Studies on Collaborative Research. 301–312.76 indexed citations
11.
Zhan, Justin, et al.. (2006). How To Construct Support Vector Machines Without Breaching Privacy.3 indexed citations
12.
Matwin, Stan, et al.. (2005). PEEP- An Information Extraction base approach for Privacy Protection in Email..9 indexed citations
13.
Zhan, Justin, LiWu Chang, & Stan Matwin. (2004). Bayesian Network Induction with Incomplete Private Data. Journal of the Association for Information Systems. 1119–1124.1 indexed citations
14.
Lethbridge, Timothy C., et al.. (2003). Applying data mining to software maintenance records. Conference of the Centre for Advanced Studies on Collaborative Research. 253–265.2 indexed citations
15.
Kersten, Gregory E., Stan Matwin, Sunil Noronha, & Mik Kersten. (2000). The Software for Cultures and the Cultures in Software. Journal of the Association for Information Systems. 509–514.8 indexed citations
16.
Lethbridge, Timothy C., et al.. (2000). Supporting maintenance of legacy software with data mining techniques. Conference of the Centre for Advanced Studies on Collaborative Research. 11.11 indexed citations
Barker, Ken, et al.. (1994). From Text to Horn Clauses: Combining Linguistic Analysis and Machine Learning.2 indexed citations
19.
Feng, Chong, et al.. (1993). Knowledge Extraction from Text: Machine Learning for Text-to-rule Translation.2 indexed citations
20.
Matwin, Stan, et al.. (1987). A Logic-Based Tools for Negotiation Support.. 499–506.7 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.