LIPIcs.FUN.2022.21.pdf
- Filesize: 0.74 MB
- 13 pages
Document
Fun with FUN
Authors
-
Part of:
Volume:
11th International Conference on Fun with Algorithms (FUN 2022)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Fun with Algorithms (FUN) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-05-23
File
Document Identifiers
Acknowledgements
This work was done at LINCS (https://www.lincs.fr/).
Cite AsGet BibTex
Fabien Mathieu and Sébastien Tixeuil. Fun with FUN. In 11th International Conference on Fun with Algorithms (FUN 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 226, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.FUN.2022.21
https://doi.org/10.4230/LIPIcs.FUN.2022.21
Abstract
The notions of scientific community and research field are central elements for researchers and the articles they publish. We propose to explore the evolution of the FUN conference community since its creation from the articles listed in DBLP, authors, program committees, and advertised themes, by means of a novel symmetric embedding, and carefully crafted software tools. Our results make it possible on the one hand to better understand the evolution of the community, and on the other hand to easily integrate new themes or researchers during future editions.
Subject Classification
ACM Subject Classification
- Information systems → Similarity measures
- Information systems → Information extraction
Keywords
- Natural Language Processing
- Relevance Propagation
- Bibliometry
- Community
- Scientific Fields
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads
References
- A. Aizawa. An information-theoretic perspective of TF-IDF measures. Info. Proc. & Manag., 39(1):45-65, 2003. URL: https://doi.org/10.1016/S0306-4573(02)00021-3.
- S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst., 30(1-7):107-117, 1998. URL: https://doi.org/10.1016/S0169-7552(98)00110-X.
- Graham Cormode, S. Muthukrishnan, and Jinyun Yan. Scienceography: The study of how science is written. In Evangelos Kranakis, Danny Krizanc, and Flaminia L. Luccio, editors, Fun with Algorithms - 6th International Conference, FUN 2012, Venice, Italy, June 4-6, 2012. Proceedings, volume 7288 of Lecture Notes in Computer Science, pages 379-391. Springer, 2012. URL: https://doi.org/10.1007/978-3-642-30347-0_37.
- Bruno Gaume and Fabien Mathieu. PageRank Induced Topology for Real-World Networks. working paper or preprint, May 2016. URL: https://hal.archives-ouvertes.fr/hal-01322040.
- D. Hong, T. D. Huynh, and F. Mathieu. D-Iteration: diffusion approach for solving PageRank. CoRR, abs/1501.06350, 2015. URL: http://arxiv.org/abs/1501.06350.
- John H. Marburger III, Julia I. Lane, Stephanie S. Shipp, and Kaye Husbands Fealing, editors. The Science of Science Policy: A Handbook. Stanford University Press, 2011. URL: http://www.sup.org/books/title/?id=18746.
- Michael Kuhn and Roger Wattenhofer. The theoretic center of computer science. SIGACT News, 38(4):54-63, 2007. URL: https://doi.org/10.1145/1345189.1345202.
- Fabien Mathieu. Generic Information Search with a Mind of its Own (Gismo). URL: https://gismo.readthedocs.io/en/latest/.
- J. Otterbacher, G. Erkan, and D. Radev. Using random walks for question-focused sentence retrieval. In HLT/EMNLP, pages 915-922, 2005. URL: https://www.aclweb.org/anthology/H05-1115.
-
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.
- Martin Rosvall and Carl T. Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118-1123, 2008. URL: https://doi.org/10.1073/pnas.0706851105.
- Dashun Wang and Albert-László Barabási. The Science of Science. Cambridge University Press, 2021. URL: https://doi.org/10.1017/9781108610834.