Published articles

  1. Saganowski S., Bródka P., Koziarski M., Kazienko P., Analysis of group evolution prediction in complex networks, PLOS ONE, https://arxiv.org/abs/1711.01867
  2. Kajdanowicz T., Kazienko P., Collective Classification Chapter in Encyclopedia of Social Network Analysis and Mining, 2018 Edition. https://link.springer.com/referenceworkentry/10.1007%2F978-1-4939-7131-2_45. 
  3. Morzy M., Kajdanowicz T., Kazienko P., Miebs G., Rusin A., Priority Attachment: a Comprehensive Mechanism for Generating Network, Scientific Reports, 2019, https://www.nature.com/articles/s41598-019-40015-9.
  4. Augustyniak Ł., Kajdanowicz T., Kazienko P., Aspect Detection using Word Embedding with (bi)LSTM and CRF, IEEE AIKE 2019. https://arxiv.org/abs/1909.01276
  5. Saha, S., Chatterjee, P., Basu, S., Nasipuri, M., & Plewczynski, D. (2019). FunPred 3.0: improved protein function prediction using protein interaction network, PeerJ, 7, e6830, https://peerj.com/articles/6830/#conclusions
  6. Bartusiak R., Augustyniak Ł., Kajdanowicz T., Kazienko P., Piasecki M.: WordNet2Vec: Corpora Agnostic Word Vectorization Method. Neurocomputing, Vol. 326-327, 2019, pp. 141-150. arXiv https://doi.org/10.1016/j.neucom.2017.01.121
  7. Oleszkiewicz W., Kairouz P., Piczak K., Rajagopal R., Trzciński T. (2019) Siamese Generative Adversarial Privatizer for Biometric Data. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11365. Springer, Cham, https://arxiv.org/abs/1804.08757
  8. Chołoniewski J., Sienkiewicz J., Leban G., Hołyst J.A.,(2019), Modelling of temporal fluctuation scaling in online news network with independent cascade model, Physica A: Statistical Mechanics and its Applications Vol. 523, 129-144, https://arxiv.org/pdf/1810.06425.pdf
  9. Weskida, M., Michalski, R.: Finding Influentials in Social Networks using Evolutionary Algorithm. Journal of Computational Science (JCR-listed journal), Vol. 31, pp. 77-85 (2019) https://doi.org/10.1016/j.jocs.2018.12.010
  10. Morzy, M., & Kajdanowicz, T. (2018). Graph energies of egocentric networks and their correlation with vertex centrality measures. Entropy, 20(12), 1–18. https://doi.org/10.3390/e20120916
  11. Szymanski, P., & Kajdanowicz, T. (2019), scikit-multilearn: A scikit-based Python environment for performing multi-label classification, Journal of Machine Learning Research 20, 1-22 (http://jmlr.org/papers/volume20/17-100/17-100.pdf)
  12. Herga Z., Doyle C., Dipple S., Nasman C., Korniss G., Szymanski B., Brank J., Rupnik J., Mladenic D., Building Client’s Risk Profile Based on Call Detail Records, accepted at SiKDD conference (https://is.ijs.si/gallery_detailed.php?id=5)
  13. Augustyniak Ł., Kajdanowicz T., Kazienko P., Extracting Aspects Hierarchies using Rhetorical Structure Theory, MLNLP  2018, Sanya, China, December 21-23, 2018. https://arxiv.org/abs/1909.01800
  14. Gajewski Ł., Suchecki K.,  Hołyst J.A. (2019), Multiple propagation paths enhance locating the source of diffusion in complex networks, Physica A: Statistical Mechanics and its Applications, Volume 519, 34-41, https://arxiv.org/abs/1901.02931v2
  15. Siudem G., Hołyst J.A. (2019), Diffusion on hierarchical systems of weakly-coupled networks, Physica A-Statistical Mechanics and Its Applications, Vol. 513, 675-686, https://arxiv.org/abs/1303.2650
  16. Biecek P., (2018), DALEX: explainers for complex predictive models in R, The Journal of Machine Learning Research, 19 (1), 3245-3249, http://www.jmlr.org/papers/volume19/18-416/18-416.pdf
  17. Kulisiewicz M., Kazienko P., Szymański B.K., Michalski R. (2018), Entropy Measures of Human Communication Dynamics. Scientific Reports, 2018, https://arxiv.org/abs/1801.04528, https://www.nature.com/articles/s41598-018-32571-3
  18. Jankowski, J., Szymanski, B. K., Kazienko, P., Michalski, R., & Bródka, P. (2018). Probing Limits of Information Spread with Sequential Seeding. Scientific Reports, 8(1), 13996. https://doi.org/10.1038/s41598-018-32081-2
  19. Bródka P.,  Chmiel A., Magnani M., Ragozini G.,  Quantifying layer similarity in multiplex networks: a systematic study, Royal Society Open Science, 2018 vol: 5 (8) pp: 171747, http://rsos.royalsocietypublishing.org/content/5/8/171747.
  20. Sienkiewicz J., Soja K., Hołyst, J. A., Sloot P. M. A. (2018),  Categorical and Geographical Separation in Science, Scientific Reports 8,  8253, https://www.nature.com/articles/s41598-018-26511-4.pdf
  21. Krawczyk M., Kułakowski K., Hołyst, J. A. (2018), Hierarchical partitions of social networks between rivaling leaders, PLOS ONE, 13(3): e0193715,  http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193715
  22. Paluch, R., Suchecki, K., Lu, X., Szymański, B. K., Hołyst, J. A. (2018), Fast and accurate detection of spread source in large complex networks, Scientific Reports, Vol. 8, No. 2508, https://www.nature.com/articles/s41598-018-20546-3?WT.feed_name=subjects_physics
  23. Klemiński R., Kazienko P., Identifying Promising Research Topics in Computer Science, 4th European Network Intelligence Conference – 11.-12. September 2017, Duisburg, https://link.springer.com/chapter/10.1007/978-3-319-90312-5_16.
  24. Kajdanowicz T., Tagowski K., Falkiewicz M., Kazienko P., Incremental Learning in Dynamic Networks for Node Classification, 4th European Conference on Network Intelligence, Duisburg, Germany, in press, 2017, https://link.springer.com/chapter/10.1007/978-3-319-90312-5_9.
  25. Szymański, P., Kajdanowicz, T (2017). A Network Perspective on Stratification of Multi-Label Data. Proceedings of Machine Learning Research, 74, 22–35. Retrieved from http://proceedings.mlr.press/v74/szymański17a/szymański17a.pdf
  26. Chmiel A., Sienkiewicz J., Sznajd-Weron K. (2017) , Tricriticality in the q-neighbor Ising model on a partially duplex clique, Physical Review E, Vol. 96, 062137, https://arxiv.org/abs/1611.07938
  27. Erlandsson F., Bródka P., Boldt M., Johnson H., Do we really need to catch them all? A new User-guided Social Media Crawling method, Entropy, 2017, http://www.mdpi.com/1099-4300/19/12/686
  28. Erlandsson F., Bródka P., Borg A., Seed selection for information cascade in multilayer networks, The 6th International Conference on Complex Networks and Their Applications, November 29 – December 01 2017 Lyon,  France, https://arxiv.org/abs/1710.04391, paper
  29. Trzcinski T., Bielski A., Cyrta P., Zak M., SocialML: machine learning for social media video creators, Neural Information Processing Systems Conference (NIPS) 2017 – Workshop on Machine Learning for Creativity and Design, Long Beach, California, USA, December 8, https://nips2017creativity.github.io/doc/SocialML.pdf
  30. Jankowski J., Michalski R., Bródka P., A multilayer network dataset of interaction and influence spreading in a virtual world, Scientific Data  https://www.nature.com/articles/sdata2017144
  31. Morzy M., Kajdanowicz T., Kazienko P. (2017), On Measuring the Complexity of Networks: Kolmogorov Complexity vs. Entropy, Complexity, https://www.hindawi.com/journals/complexity/2017/3250301
  32. Toruniewska J., Kułakowski K., Suchecki K., Hołyst J.A. (2017), Coupling of link- and node-ordering in the coevolving voter model, Physical Review E, Vol. 96, No. 4, 042306, http://arxiv.org/abs/1711.09004
  33. Jankowski J., Bródka P., Michalski R., Kazienko P. (2017),  Seeds Buffering in Information Spreading Processes. SocInfo2017 – 9th International Conference on Social Informatics. Oxford, UK, September 13-15, Lecture Notes on Computer Science LNCS , Springer, 2017, Available at: https://arxiv.org/abs/1709.04863
  34. Jankowski, J., Bródka, P., Kazienko, P., Szymanski, B., Michalski, R., & Kajdanowicz, T. (2017), Balancing Speed and Coverage by Sequential Seeding in Complex Networks, Scientific Reports, vol. 7, art. 891.  Available at: https://www.nature.com/articles/s41598-017-00937-8
  35. Augustyniak Ł., Rajda K., Kajdanowicz T. (2017), Method for aspect-based sentiment annotation using rhetorical analysis, 9th Asian Conference, ACIIDS 2017, Kanazawa, Japan, April 3-5, Proceedings, Part I, pp 772-781, Available at:  https://link.springer.com/chapter/10.1007/978-3-319-54472-4_72  and on arxiv
  36. Morzy M., Kajdanowicz T., Szymański B. (2016), Benford’s distribution in complex networks, Scientific Reports, vol. 6, art. 34917. Available at: http://www.nature.com/articles/srep34917
  37. Chołoniewski J., Leban G., Macek S., Rehar A.(2016), Information flow between news articles: Slovene media case study, Informatica. An International Journal of Computing and Informatics, D, 13-16.  Available at: http://ailab.ijs.si/dunja/SiKDD2016/Papers/Choloniewski_InformationFlow.pdf
  38. Gajewski Ł.G., Chołoniewski J., Hołyst, J. A. (2016), Key Courses of Academic Curriculum Uncovered by Data Mining of Students’ Grades, ACTA PHYSICA POLONICA A, Vol. 129, no. 5, http://www.if.pw.edu.pl/~jholyst/data/a129z5p32.pdf
  39. Szymański, P., Kajdanowicz, T., & Kersting, K. (2016), How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification? Entropy, 18(8), 282. Available at: http://doi.org/10.3390/e18080282

Accepted

  1. Halder A.K., Denkiewicz M., Sengupta K., Basub S., Plewczynski D., Aggregated network centrality shows non-random structure of genomic and proteomic networks, Elsevier

Submitted

    1. Halder A.K., Bandyopadhyay S.S., Chatterjee P., Nasipuri M., Plewczynski D., Basu S., JUPPI: A multi-level feature based method for PPI prediction and a refined strategy for performance assessment,
    2. Augustyniak, Ł., Kajdanowicz, T., Kazienko, P., Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings, https://arxiv.org/abs/1909.04917
    3. Atkisson C., Górski P.J., Jackson M.O., Hołyst J.A., D’Souza R.M., Why understanding multiplex social network structuring processes will help us better understand the evolution of human behavior, submitted to Evolutionary Anthropology, https://arxiv.org/abs/1903.11183
    4. Liu W., Saganowski S., Kazienko P., Cheong S. A., Using Machine Learning to Predict the Evolution of Physics Research, submitted to Scientific Reports, https://arxiv.org/abs/1810.12116
    5. Czaplicka A., Hołyst, J. A., From equality to diversity – bottom-up approach for hierarchy growth, submitted to Physical Review E, https://arxiv.org/abs/1707.00985
    6. Morzy M., Kajdanowicz T., Kazienko P., Miebs G., and Rusin A., Priority Rank: Universal Network Model for Generating Synthetic and Re-creating Empirical Networks, in preparation, to be submitted to Scientific Reports, 2017.
    7. Doyle C., Herga Z., Dipple S., Szymanski B., Kornissa G., and Mladenic D., Predicting complex user behavior from CDR based social networks, submitted to Information Sciences.
    8. Klemiński. R., Kazienko P., Impact of Citation Networks on Keyword Filtering in Computer Science Literature.

Under Preparation

  1. Bródka P., Musial K., Jankowski J., Interacting spreading processes in multilayer networks, arXiv:1903.05932.
  2. Paluch R., Chołoniewski J., Suchecki K., Hołyst, J.A., Leban, G., Fuart, F., Karlovcec, M., Rupnik, J., Grobelnik,M., Hoekstra, A., Sloot, P., Sobkowicz, P.,  Toczyski, P., Does the rise of new media explain the expansion of old media coverage ?
  3. Kajdanowicz T., Kazienko P., Popiel A., Chawla N., Fusion Methods for Node Classification in Multiplex Networks.
  4. Michalski R., Szymanski B.K., Kazienko P., Lebiere C., Lizardo O., Kulisiewicz M.: Temporal Social Networks Through the Prism of Cognition, https://arxiv.org/abs/1806.04658.
  5. Augustyniak, Ł., Kajdanowicz, T., Kazienko, P., Using Rhetorical Structure Theory for Sentiment-oriented Abstractive Summarization – in progress.
  6. Szymański, P, Kajdanowicz T., Chawla, N., LNEMLC: Label Network Embeddings for Multi-Label Classification.
  7. Kajdanowicz, T., Tagowski, K., Falkiewicz M., Bielak P., Kazienko, P., Chawla N. V., Incremental embedding for temporal networks – in progress. https://arxiv.org/abs/1904.03423.
  8. Górski P.J., Bochenina K., Hołyst J.A., D’Souza R.D., Coevolution of agents’ attributes, social links and triadic relations for structural balance.
  9. Górski P.J., Atkisson C., Hołyst J.A., How attributes can reduce polarization in social groups.

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