Publication List (by Type)

This list is also available as a BibTeX file. Many preprints are available for download here and here.

Preprints/Drafts (Selection)

  1. Brzozowski L., Siudem G., Gagolewski M., Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering, arXiv, 2023, under review (preprint), DOI:10.48550/arXiv.2303.12212

Journal Articles (54)

  1. Gagolewski M., Normalised clustering accuracy: An asymmetric external cluster validity measure, Journal of Classification, 2024, in press, DOI:10.1007/s00357-024-09482-2

  2. Gagolewski M., Cena A., Bartoszuk M., Brzozowski L., Clustering with minimum spanning trees: How good can it be?, Journal of Classification, 2024, in press, DOI:10.1007/s00357-024-09483-1

  3. Bertoli-Barsotti L., Gagolewski M., Siudem G., Żogała-Siudem B., Equivalence of inequality indices in the three-dimensional model of informetric impact, Journal of Informetrics 18(4), 101566, 2024, DOI:10.1016/j.joi.2024.101566

  4. Bertoli-Barsotti L., Gagolewski M., Siudem G., Żogała-Siudem B., Gini-stable Lorenz curves and their relation to the generalised Pareto distribution, Journal of Informetrics 18(2), 101499, 2024, DOI:10.1016/j.joi.2024.101499

  5. Wu J-Z., Beliakov G., James S., Gagolewski M., Random generation of linearly constrained fuzzy measures and domain coverage performance evaluation, Information Sciences 659, 120080, 2024, DOI:10.1016/j.ins.2023.120080

  6. Gagolewski M., Cena A., James S., Beliakov G., Hierarchical clustering with OWA-based linkages, the Lance–Williams formula, and dendrogram inversions, Fuzzy Sets and Systems 473, 108740, 2023, DOI:10.1016/j.fss.2023.108740

  7. Boczek M., Gagolewski M., Kaluszka M., Okolewski A., A benchmark-type generalization of the Sugeno integral with applications in bibliometrics, Fuzzy Sets and Systems 466, 108479, 2023, DOI:10.1016/j.fss.2023.01.014

  8. Żogała-Siudem B., Cena A., Siudem G., Gagolewski M., Interpretable reparameterisations of citation models, Journal of Informetrics 17(1), 101355, 2023, DOI:10.1016/j.joi.2022.101355

  9. Gagolewski M., A framework for benchmarking clustering algorithms, SoftwareX 20, 101270, 2022, DOI:10.1016/j.softx.2022.101270, URL:https://clustering-benchmarks.gagolewski.com/

  10. Siudem G., Nowak P., Gagolewski M., Power laws, the Price Model, and the Pareto type-2 distribution, Physica A: Statistical Mechanics and its Applications 606, 128059, 2022, DOI:10.1016/j.physa.2022.128059

  11. Gagolewski M., stringi: Fast and portable character string processing in R, Journal of Statistical Software 103(2), 1–59, 2022, DOI:10.18637/jss.v103.i02, URL:https://stringi.gagolewski.com/

  12. Beliakov G., Gagolewski M., James S., Reduction of variables and constraints in fitting antibuoyant fuzzy measures to data using linear programming, Fuzzy Sets and Systems 451, 266–284, 2022, DOI:10.1016/j.fss.2022.06.025

  13. Geras A., Siudem G., Gagolewski M., Time to vote: Temporal clustering of user activity on Stack Overflow, Journal of the Association for Information Science and Technology 73(12), 1681–1691, 2022, DOI:10.1002/asi.24658

  14. Gagolewski M., Żogała-Siudem B., Siudem G., Cena A., Ockham’s index of citation impact, Scientometrics 127, 2829–2845, 2022, DOI:10.1007/s11192-022-04345-2

  15. Mrowiński M., Gagolewski M., Siudem G., Accidentality in journal citation patterns, Journal of Informetrics 16(4), 101341, 2022, DOI:10.1016/j.joi.2022.101341

  16. Cena A., Gagolewski M., Siudem G., Żogała-Siudem B., Validating citation models by proxy indices, Journal of Informetrics 16(2), 101267, 2022, DOI:10.1016/j.joi.2022.101267

  17. Gagolewski M., Bartoszuk M., Cena A., Are cluster validity measures (in)valid?, Information Sciences 581, 620–636, 2021, DOI:10.1016/j.ins.2021.10.004, URL:https://github.com/gagolews/optim_cvi

  18. Beliakov G., Gagolewski M., James S., Hierarchical data fusion processes involving the Möbius representation of capacities, Fuzzy Sets and Systems 433, 1–21, 2022, DOI:10.1016/j.fss.2021.02.006

  19. Bartoszuk M., Gagolewski M., T-norms or t-conorms? How to aggregate similarity degrees for plagiarism detection, Knowledge-Based Systems 231, 107427, 2021, DOI:10.1016/j.knosys.2021.107427

  20. Lasek J., Gagolewski M., Interpretable sports team rating models based on the gradient descent algorithm, International Journal of Forecasting 37(3), 1061–1071, 2021, DOI:10.1016/j.ijforecast.2020.11.008

  21. Gagolewski M., genieclust: Fast and robust hierarchical clustering, SoftwareX 15, 100722, 2021, DOI:10.1016/j.softx.2021.100722, URL:https://genieclust.gagolewski.com/

  22. Pérez-Fernández R., Gagolewski M., De Baets B., On the aggregation of compositional data, Information Fusion 73, 103–110, 2021, DOI:10.1016/j.inffus.2021.02.021

  23. Beliakov G., Gagolewski M., James S., DC optimization for constructing discrete Sugeno integrals and learning nonadditive measures, Optimization 69(12), 2515–2534, 2020, DOI:10.1080/02331934.2019.1705300

  24. Bartoszuk M., Gagolewski M., SimilaR: R Code Clone and Plagiarism Detection, R Journal 12(1), 367–385, 2020, DOI:10.32614/RJ-2020-017, URL:https://CRAN.R-project.org/package=SimilaR

  25. Siudem G., Żogała-Siudem B., Cena A., Gagolewski M., Three dimensions of scientific impact, Proceedings of the National Academy of Sciences of the United States of America (PNAS) 117, 13896–13900, 2020, DOI:10.1073/pnas.2001064117

  26. Coroianu L., Fullér R., Gagolewski M., James S., Constrained ordered weighted averaging aggregation with multiple comonotone constraints, Fuzzy Sets and Systems 395, 21–39, 2020, DOI:10.1016/j.fss.2019.09.006

  27. Cena A., Gagolewski M., Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages, Information Sciences 520, 324–336, 2020, DOI:10.1016/j.ins.2020.02.025

  28. Gagolewski M., Pérez-Fernández R., De Baets B., An inherent difficulty in the aggregation of multidimensional data, IEEE Transactions on Fuzzy Systems 28, 602–606, 2020, DOI:10.1109/TFUZZ.2019.2908135

  29. Beliakov G., Gagolewski M., James S., Robust fitting for the Sugeno integral with respect to general fuzzy measures, Information Sciences 514, 449–461, 2020, DOI:10.1016/j.ins.2019.11.024

  30. Geras A., Siudem G., Gagolewski M., Should we introduce a dislike button for academic papers?, Journal of the Association for Information Science and Technology 71(2), 221–229, 2020, DOI:10.1002/ASI.24231

  31. Coroianu L., Gagolewski M., Grzegorzewski P., Piecewise linear approximation of fuzzy numbers: Algorithms, arithmetic operations and stability of characteristics, Soft Computing 23(19), 9491–9505, 2019, DOI:10.1007/s00500-019-03800-2, URL:https://CRAN.R-project.org/package=FuzzyNumbers

  32. Beliakov G., Gagolewski M., James S., Aggregation on ordinal scales with the Sugeno integral for biomedical applications, Information Sciences 501, 377–387, 2019, DOI:10.1016/j.ins.2019.06.023

  33. Pérez-Fernández R., De Baets B., Gagolewski M., A taxonomy of monotonicity properties for the aggregation of multidimensional data, Information Fusion 52, 322–334, 2019, DOI:10.1016/j.inffus.2019.05.006

  34. Gagolewski M., James S., Beliakov G., Supervised learning to aggregate data with the Sugeno integral, IEEE Transactions on Fuzzy Systems 27(4), 810–815, 2019, DOI:10.1109/TFUZZ.2019.2895565

  35. Beliakov G., Gagolewski M., James S., Pace S., Pastorello N., Thilliez E., Vasa R., Measuring traffic congestion: An approach based on learning weighted inequality, spread and aggregation indices from comparison data, Applied Soft Computing 67, 910–919, 2019, DOI:10.1016/j.asoc.2017.07.014

  36. Lasek J., Gagolewski M., The efficacy of league formats in ranking teams, Statistical Modelling 18(5–6), 411–435, 2018, DOI:10.1177/1471082X18798426

  37. Gagolewski M., Penalty-based aggregation of multidimensional data, Fuzzy Sets and Systems 325, 4–20, 2017, DOI:10.1016/j.fss.2016.12.009

  38. Mesiar R., Gagolewski M., H-index and other Sugeno integrals: Some defects and their compensation, IEEE Transactions on Fuzzy Systems 24(6), 1668–1672, 2016, DOI:10.1109/TFUZZ.2016.2516579

  39. Beliakov G., Gagolewski M., James S., Penalty-based and other representations of economic inequality, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24(Suppl.1), 1–23, 2016, DOI:10.1142/S0218488516400018

  40. Gagolewski M., Bartoszuk M., Cena A., Genie: A new, fast, and outlier-resistant hierarchical clustering algorithm, Information Sciences 363, 8–23, 2016, DOI:10.1016/j.ins.2016.05.003, URL:https://genieclust.gagolewski.com/

  41. Lasek J., Szlavik Z., Gagolewski M., Bhulai S., How to improve a team’s position in the FIFA ranking – A simulation study, Journal of Applied Statistics 43(7), 1349–1368, 2016, DOI:10.1080/02664763.2015.1100593

  42. Żogała-Siudem B., Siudem G., Cena A., Gagolewski M., Agent-based model for the bibliometric h-index – Exact solution, European Physical Journal B 89(21), 2016, DOI:10.1140/epjb/e2015-60757-1

  43. Cena A., Gagolewski M., Mesiar R., Problems and challenges of information resources producers’ clustering, Journal of Informetrics 9(2), 2015, DOI:10.1016/j.joi.2015.02.005

  44. Cena A., Gagolewski M., OM3: Ordered maxitive, minitive, and modular aggregation operators – Axiomatic and probabilistic properties in an arity-monotonic setting, Fuzzy Sets and Systems 264, 138–159, 2015, DOI:10.1016/j.fss.2014.04.001

  45. Gagolewski M., Spread measures and their relation to aggregation functions, European Journal of Operational Research 241(2), 469–477, 2015, DOI:10.1016/j.ejor.2014.08.034

  46. Gagolewski M., Mesiar R., Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem, Information Sciences 263, 166–174, 2014, DOI:10.1016/j.ins.2013.12.004

  47. Gagolewski M., On the relationship between symmetric maxitive, minitive, and modular aggregation operators, Information Sciences 221, 170–180, 2013, DOI:10.1016/j.ins.2012.09.005

  48. Coroianu L., Gagolewski M., Grzegorzewski P., Nearest piecewise linear approximation of fuzzy numbers, Fuzzy Sets and Systems 233, 26–51, 2013, DOI:10.1016/j.fss.2013.02.005, URL:https://CRAN.R-project.org/package=FuzzyNumbers

  49. Gagolewski M., Scientific impact assessment cannot be fair, Journal of Informetrics 7(4), 792–802, 2013, DOI:10.1016/j.joi.2013.07.001

  50. Gagolewski M., Mesiar R., Aggregating different paper quality measures with a generalized h-index, Journal of Informetrics 6(4), 566–579, 2012, DOI:10.1016/j.joi.2012.05.001

  51. Gagolewski M., Bibliometric impact assessment with R and the CITAN package, Journal of Informetrics 5(4), 678–692, 2011, DOI:10.1016/j.joi.2011.06.006, URL:https://CRAN.R-project.org/package=CITAN

  52. Gagolewski M., Grzegorzewski P., Possibilistic analysis of arity-monotonic aggregation operators and its relation to bibliometric impact assessment of individuals, International Journal of Approximate Reasoning 52(9), 1312–1324, 2011, DOI:10.1016/j.ijar.2011.01.010

  53. Gagolewski M., Grzegorzewski P., A geometric approach to the construction of scientific impact indices, Scientometrics 81(3), 617–634, 2009, DOI:10.1007/s11192-008-2253-y

  54. Rowiński T., Gagolewski M., Preferencje i postawy wobec pomocy online (Attitudes towards online counselling and psychotherapy), Studia Psychologica UKSW 7, 195–210, 2007, in Polish

Research Monographs and Textbooks (7)

For many students around the world, educational resources are hardly affordable. Therefore, I have decided that my recent books should remain independent, non-profit, open-access projects. Whilst, for some people, the presence of a “designer tag” from a major publisher might still be a proxy for quality, it is my hope that some of the following items will prove useful to those who seek knowledge for knowledge’s sake. Please spread the word about these projects. Thank you.

  1. Gagolewski M., Deep R Programming, Melbourne, v1.0.0 edition, 2023, 456 pp., 🔓, DOI:10.5281/zenodo.7490464, URL:https://deepr.gagolewski.com/

  2. Gagolewski M., Minimalist Data Wrangling with Python, Melbourne, v1.0.3 edition, 2023, 442 pp., 🔓, DOI:10.5281/zenodo.6451068, URL:https://datawranglingpy.gagolewski.com/

  3. Gagolewski M., Algorytmy i postawy programowania w języku C++ (Introduction to Algorithms and Programming in C++), Melbourne, v1.2.0 edition, 2022, 209 pp., 🇵🇱 🔓, DOI:10.5281/zenodo.6451054, URL:https://github.com/gagolews/aipp

  4. Gagolewski M., Bartoszuk M., Cena A., Przetwarzanie i analiza danych w języku Python (Data Processing and Analysis in Python), Wydawnictwo Naukowe PWN, Warsaw, 2016, 369 pp., 🇵🇱, URL:https://github.com/gagolews/Analiza_danych_w_jezyku_Python

  5. Gagolewski M., Programowanie w języku R. Analiza danych, obliczenia, symulacje (R Programming. Data Analysis, Computing, Simulations), Wydawnictwo Naukowe PWN, Warsaw, 2nd edition, 2016, 550 pp., 🇵🇱, URL:https://github.com/gagolews/Programowanie_w_jezyku_R

  6. Gagolewski M., Data Fusion: Theory, Methods, and Applications, Institute of Computer Science, Polish Academy of Sciences, Warsaw, 2015, 290 pp., 🔓, URL:https://github.com/gagolews/datafusion

  7. Grzegorzewski P., Gagolewski M., Bobecka-Wesołowska K., Wnioskowanie statystyczne z wykorzystaniem środowiska R (Statistical Inference with R), Politechnika Warszawska, Warsaw, 2014, 183 pp., 🇵🇱 🔓

Edited Volumes (3)

  1. Halaš R., Gagolewski M., Mesiar R. (eds.), New Trends in Aggregation Theory, Springer, 2019, 348 pp., DOI:10.1007/978-3-030-19494-9

  2. Ferraro M., Giordani P., Vantaggi B., Gagolewski M., Gil M., Grzegorzewski P., Hryniewicz O. (eds.), Soft Methods for Data Science, Springer, 2017, 535 pp., DOI:10.1007/978-3-319-42972-4

  3. Grzegorzewski P., Gagolewski M., Hryniewicz O., Gil M. (eds.), Strengthening Links Between Data Analysis and Soft Computing, Springer, 2015, 294 pp., DOI:10.1007/978-3-319-10765-3

Papers in Conference Proceedings (35)

  1. Beliakov G., James S., Wu J-Z., Gagolewski M., A Tractable Approach to Fitting the Choquet Integral for Explainable Prediction and Analysis, in: Proc. 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2024, pp. 10611753, DOI:10.1109/FUZZ-IEEE60900.2024.10611753

  2. Coroianu L., Gagolewski M., Penalty-based data aggregation in real normed vector spaces, in: Halaš R. et al. (eds.), New Trends in Aggregation Theory, Springer, 2019, pp. 160–171, DOI:10.1007/978-3-030-19494-9_15

  3. Beliakov G., Gagolewski M., James S., Least median of squares (LMS) and least trimmed squares (LTS) fitting for the weighted arithmetic mean, in: Medina J. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, Springer, 2018, pp. 367–378, DOI:10.1007/978-3-319-91476-3_31

  4. Gagolewski M., James S., Fitting symmetric fuzzy measures for discrete Sugeno integration, in: Kacprzyk J. et al. (eds.), Advances in Fuzzy Logic and Technology 2017, Springer, 2018, pp. 104–116, DOI:10.1007/978-3-319-66824-6_10

  5. Bartoszuk M., Gagolewski M., Binary aggregation functions in software plagiarism detection, in: Proc. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2017, pp. 8015582, DOI:10.1109/FUZZ-IEEE.2017.8015582

  6. Cena A., Gagolewski M., OWA-based linkage and the Genie correction for hierarchical clustering, in: Proc. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2017, pp. 8015652, DOI:10.1109/FUZZ-IEEE.2017.8015652

  7. Gagolewski M., Cena A., Bartoszuk M., Hierarchical clustering via penalty-based aggregation and the Genie approach, in: Torra V. et al. (eds.), Modeling Decisions for Artificial Intelligence, Springer, 2016, pp. 191–202, DOI:10.1007/978-3-319-45656-0_16

  8. Cena A., Gagolewski M., Fuzzy k-minpen clustering and k-nearest-minpen classification procedures incorporating generic distance-based penalty minimizers, in: Carvalho J. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2016, pp. 445–456, DOI:10.1007/978-3-319-40581-0_36

  9. Bartoszuk M., Beliakov G., Gagolewski M., James S., Fitting aggregation functions to data: Part I – Linearization and regularization, in: Carvalho J. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2016, pp. 767–779, DOI:10.1007/978-3-319-40581-0_62

  10. Bartoszuk M., Beliakov G., Gagolewski M., James S., Fitting aggregation functions to data: Part II – Idempotization, in: Carvalho J. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2016, pp. 780–789, DOI:10.1007/978-3-319-40581-0_63

  11. Bartoszuk M., Gagolewski M., Detecting similarity of R functions via a fusion of multiple heuristic methods, in: Alonso J., Bustince H., Reformat M. (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 419–426, DOI:10.2991/ifsa-eusflat-15.2015.61

  12. Cena A., Gagolewski M., A K-means-like algorithm for informetric data clustering, in: Alonso J., Bustince H., Reformat M. (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 536–543, DOI:10.2991/ifsa-eusflat-15.2015.77

  13. Gagolewski M., Sugeno integral-based confidence intervals for the theoretical h-index, in: Grzegorzewski P. et al. (eds.), Strengthening Links Between Data Analysis and Soft Computing, Springer, 2015, pp. 233–240, DOI:10.1007/978-3-319-10765-3_28

  14. Gagolewski M., Normalized WD\(_p\)WAM and WD\(_p\)OWA spread measures, in: Alonso J., Bustince H., Reformat M. (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 210–216, DOI:10.2991/ifsa-eusflat-15.2015.32

  15. Gagolewski M., Lasek J., The use of fuzzy relations in the assessment of information resources producers’ performance, in: Proc. 7th IEEE International Conference Intelligent Systems IS’2014, Vol. 2: Tools, Architectures, Systems, Applications, Springer, 2015, pp. 289–300, DOI:10.1007/978-3-319-11310-4_25

  16. Gagolewski M., Lasek J., Learning experts’ preferences from informetric data, in: Alonso J., Bustince H., Reformat M. (eds.), Proc. IFSA/EUSFLAT’15, Atlantis Press, 2015, pp. 484–491, DOI:10.2991/ifsa-eusflat-15.2015.70

  17. Lasek J., Gagolewski M., Estimation of tournament metrics for association football league formats, in: Selected problems in information technologies (Proc. ITRIA’15 vol. 2), Institute of Computer Science, Polish Academy of Sciences, 2015, pp. 67–78

  18. Lasek J., Gagolewski M., The winning solution to the AAIA’15 Data Mining Competition: Tagging firefighter activities at a fire scene, in: Ganzha M., Maciaszek L., Paprzycki M. (eds.), Proc. FedCSIS’15, IEEE, 2015, pp. 375–380, DOI:10.15439/2015F418

  19. Cena A., Gagolewski M., Clustering and aggregation of informetric data sets, in: Computational methods in data analysis (Proc. ITRIA’15 vol. 1), Institute of Computer Science, Polish Academy of Sciences, 2015, pp. 5–26

  20. Gagolewski M., Some issues in aggregation of multidimensional data, in: Baczyński M., De Baets B., Mesiar R. (eds.), Proc. 8th International Summer School on Aggregation Operators (AGOP 2015), University of Silesia, 2015, pp. 127–132

  21. Cena A., Gagolewski M., Aggregation and soft clustering of informetric data, in: Baczyński M., De Baets B., Mesiar R. (eds.), Proc. 8th International Summer School on Aggregation Operators (AGOP 2015), University of Silesia, 2015, pp. 79–84

  22. Coroianu L., Gagolewski M., Grzegorzewski P., Adabitabar Firozja M., Houlari T., Piecewise linear approximation of fuzzy numbers preserving the support and core, in: Laurent A. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part II, Springer, 2014, pp. 244–254, DOI:10.1007/978-3-319-08855-6_25

  23. Bartoszuk M., Gagolewski M., A fuzzy R code similarity detection algorithm, in: Laurent A. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part III, Springer, 2014, pp. 21–30, DOI:10.1007/978-3-319-08852-5_3

  24. Gagolewski M., Dębski M., Nowakiewicz M., Efficient algorithm for computing certain graph-based monotone integrals: The \(l_p\)-indices, in: Mesiar R., Bacigal T. (eds.), Proc. Uncertainty Modeling, STU Bratislava, 2013, pp. 17–23

  25. Cena A., Gagolewski M., OM3: Ordered maxitive, minitive, and modular aggregation operators – Part I: Axiomatic analysis under arity-dependence, in: Bustince H. et al. (eds.), Aggregation Functions in Theory and in Practise, Springer, 2013, pp. 93–103, DOI:10.1007/978-3-642-39165-1_13

  26. Cena A., Gagolewski M., OM3: Ordered maxitive, minitive, and modular aggregation operators – Part II: A simulation study, in: Bustince H. et al. (eds.), Aggregation Functions in Theory and in Practise, Springer, 2013, pp. 105–115, DOI:10.1007/978-3-642-39165-1_14

  27. Gagolewski M., Statistical hypothesis test for the difference between Hirsch indices of two Pareto-distributed random samples, in: Kruse R. et al. (eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer, 2013, pp. 359–367, DOI:10.1007/978-3-642-33042-1_39

  28. Gagolewski M., On the relation between effort-dominating and symmetric minitive aggregation operators, in: Greco S. et al. (eds.), Advances in Computational Intelligence, Part III, Springer, 2012, pp. 276–285, DOI:10.1007/978-3-642-31718-7_29

  29. Gagolewski M., Grzegorzewski P., Axiomatic characterizations of (quasi-) L-statistics and S-statistics and the Producer Assessment Problem, in: Galichet S. et al. (eds.), Proc. EUSFLAT/LFA’11, Atlantis Press, 2011, pp. 53–58, DOI:10.2991/eusflat.2011.112

  30. Rowiński T., Gagolewski M., Internet a kryzys, in: Jankowska M., Starzomska M. (eds.), Kryzys: Pułapka czy szansa?, WN Akapit, Warsaw, 2011, pp. 211–224, in Polish

  31. Gagolewski M., Grzegorzewski P., Arity-monotonic extended aggregation operators, in: Hüllermeier E. et al. (eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer, 2010, pp. 693–702, DOI:10.1007/978-3-642-14055-6_73

  32. Gagolewski M., Grzegorzewski P., S-statistics and their basic properties, in: Borgelt C. et al. (eds.), Combining Soft Computing and Statistical Methods in Data Analysis, Springer, 2010, pp. 281–288, DOI:10.1007/978-3-642-14746-3_35

  33. Gagolewski M., Grzegorzewski P., Metody i problemy naukometrii (Methods and problems of scientometrics), in: Rowiński T., Tadeusiewicz R. (eds.), Psychologia i informatyka. Synergia i kontradykcje, Wyd. UKSW, Warsaw, 2010, pp. 103–125, in Polish

  34. Gagolewski M., Grzegorzewski P., O pewnym uogólnieniu indeksu Hirscha, in: Kawalec P., Lipski P. (eds.), Kadry i infrastruktura nowoczesnej nauki: Teoria i praktyka, Proc. 1st Intl. Conf. Zarządzanie Nauką, Wydawnictwo Lubelskiej Szkoły Biznesu, Lublin, 2009, pp. 15–29, in Polish

  35. Gagolewski M., Grzegorzewski P., Possible and necessary h-indices, in: Carvalho J. et al. (eds.), Proc. IFSA/EUSFLAT’09, IFSA, 2009, pp. 1691–1695