Mike Kestemont
About
In The Last Decade
Mike Kestemont
71 papers receiving 673 citations
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 602
- Literature and Literary Theory 112
- Sociology and Political Science 94
- Language and Linguistics 87
- Information Systems 53
Countries citing papers authored by Mike Kestemont
This map shows the geographic impact of Mike Kestemont'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 Mike Kestemont with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mike Kestemont more than expected).
Fields of papers citing papers by Mike Kestemont
This network shows the impact of papers produced by Mike Kestemont. 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 Mike Kestemont. The network helps show where Mike Kestemont may publish in the future.
Co-authorship network of co-authors of Mike Kestemont
This figure shows the co-authorship network connecting the top 25 collaborators of Mike Kestemont. A scholar is included among the top collaborators of Mike Kestemont 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 Mike Kestemont. Mike Kestemont is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 18 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | Overview of the Cross-Domain Authorship Verification Task at PAN 2020. | 19 |
| 7 | Improving the Training of Deep Convolutional Neural Networks for Art Classification: from Transfer Learning to Multi-Task Learning | 1 |
| 8 | 2 | |
| 9 | Overview of the Cross-domain Authorship Attribution Task at PAN 2019. | 13 |
| 10 | Overview of the Author Identification Task at PAN-2018: Cross-domain Authorship Attribution and Style Change Detection. | 36 |
| 11 | 29 | |
| 12 | Script Identification in Medieval Latin Manuscripts Using Convolutional Neural Networks. | 2 |
| 13 | Did a Poet with Donkey Ears Write the Oldest Anthem in the World? Ideological Implications of the Computational Attribution of the Dutch National Anthem to Petrus Dathenus. | 2 |
| 14 | Digital Humanities in the BeNeLux 2015 | 1 |
| 15 | Validating Computational Stylistics in Literary Interpretation. | 1 |
| 16 | 1 | |
| 17 | The Netlog Corpus. A Resource for the Study of Flemish Dutch Internet Language | 5 |
| 18 | What Can Stylometry Learn From Its Application to Middle Dutch Literature | 5 |
| 19 | 2 | |
| 20 | Computational approaches to textual variation in medieval literature | 0 |
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.