Network Science Lab

Network Science Lab at Wrocław Tech

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About

Network Science Lab is a group of people working on various aspects of network science, starting from spreading processes (information diffusion, influence spread, opinion formation, virus spread, etc.), through interacting spreading processes, seed selection (e.g. for influence maximisation, for “vaccination”, etc.), spreading velocity (e.g. seed distribution, process control etc.), community detection and evolution, social learning and cognitive processes, network control, streaming scenarios (including embeddings), and ending on computational social science. You can meet us at most of the top conferences on complex networks and social informatics, e.g., NetSci, NetSciX, IC2S2, ASONAM, SocInfo etc.

People

Lab lead
Prof Piotr Bródka
Lab lead
Prof Radosław Michalski


PhD Student
Mateusz Nurek
PhD Student
Michał Czuba
PhD Student
Damian Serwata
PhD Student
Karol Dykiert
PhD Student
Daria Dziubałtowska
PhD Student
Stanisław Stępień
PhD Student
Mateusz Stolarski

Publications

    Articles in the International Journals

  1. Stolarski M, Piróg A, Bródka, P. (2024). Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach. Entropy, 2024; 26(11):955. https://doi.org/10.3390/e26110955
    PDF Code at GitHub
  2. Wu, J., Dai, H. N., Xuan, Q., Michalski, R., and Chen, X. (2024). Blockchain transaction data mining and its applications. IET Blockchain, 4(3), 223-225.
  3. Paradowski, M.B., Whitby, N., Czuba, M. and Bródka, P. (2024). Peer Interaction Dynamics and Second Language Learning Trajectories During Study Abroad: A Longitudinal Investigation Using Dynamic Computational Social Network Analysis. Language Learning. https://doi.org/10.1111/lang.12681
    PDF Repository with code and data
  4. Czuba, M., Nurek, M., Serwata, D., Qui, Y., Jia, M. Musiał, K., Michalski, R., Bródka, P. (2024). Network Diffusion – Framework to Simulate Spreading Processes in Complex Networks. Big Data Mining and Analytics, 7(1), ; https://doi.org/10.26599/BDMA.2024.9020010
    PDF
  5. Sadaf, A., Mathieson, L., Bródka, P., Musiał, K. (2024). A bridge between influence models and control methods. Applied Network Scienc 9, 38 https://doi.org/10.1007/s41109-024-00647-x
    PDF
  6. Nurek, M., Michalski, R., Lizardo, O., Rizoiu, MA: (2023) Predicting Relationship Labels and Individual Personality Traits from Telecommunication History in Social Networks using Hawkes Processes. IEEE Access (JCR-listed journal), Vol. 11, pp. 8492-8503
  7. Wątroba, P., Bródka, P., (2023). Influence of Information Blocking on the Spread of Virus in Multilayer Networks. Entropy 2023, 25(2), 231; https://doi.org/10.3390/e25020231
    PDF
  8. Wang, Y., Wang, X., Michalski, R., Ran, Y., Jia T. (2022): CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades. Expert Systems with Applications (JCR-listed journal), Vol. 206, 117693
  9. Michalski, R., Serwata, D., Nurek, M., Szymański, B., Kazienko, P., Jia, T.: (2022) Temporal Network Epistemology: on Reaching Consensus in Real World Setting. Chaos: An Interdisciplinary Journal of Nonlinear Science (JCR-listed journal), Vol. 32, 063135
  10. Bródka, P. Jankowski, J., & Michalski, R. (2021). Sequential seeding in multilayer networks. Chaos 31, 033130; https://doi.org/10.1063/5.0023427
    PDF Code and data at GitHub and Code Ocean Presentation
  11. Michalski, R., Szymański, B.K., Kazienko, P., Lebiere, C., Lizardo, O., Kulisiewicz, M.: (2021)Social Networks through the Prism of Cognition. Complexity (JCR-listed journal), Vol. 2021, Article ID 4963903, Wiley/Hindawi
  12. Michalski, R., Jankowski, J. & Bródka, P. (2020). Effective Influence Spreading in Temporal Networks with Sequential Seeding. IEEE Access, vol. 8, pp. 151208-151218, doi: 10.1109/ACCESS.2020.3016913.
    PDF Code and data at Code Ocean Presentation
  13. Bródka, P., Musial, K., & Jankowski, J. (2020). Interacting spreading processes in multilayer networks. IEEE Access, vol. 8, pp. 10316-10341, doi:10.1109/ACCESS.2020.2965547
    PDF
  14. Belfin, R. V., Brodka, P., Radhakrishnan, B. L., & Rejula, V. (2020). COVID-19 peak estimation and effect of nationwide lockdown in India. medRxiv, medRxiv 2020.05.09.20095919; doi: https://doi.org/10.1101/2020.05.09.20095919
    PDF
  15. Saganowski S., Bródka P., Koziarski M., Kazienko P. (2019) . Analysis of group evolution prediction in complex networks. PloS ONE, 14(10): e0224194
    PDF Data at Harvard Dataverse
  16. Jankowski, J., Waniek, M., Alshamsi, A., Bródka, P., Michalski, R. (2018). Strategic distribution of seeds to support diffusion in complex networks. PloS ONE, 13(10), e0205130
    PDF Presentation
  17. Jankowski J., Szymanski B., Kazienko P., Michalski R., Bródka P. (2018) Probing Limits of Information Spread with Sequential Seeding Scientific Reports, 8(1), 13996, DOI: 10.1038/s41598-018-32081-2
    PDF Presentation
  18. Bródka, P., Chmiel, A., Magnani, M., Ragozini, G. (2018). Quantifying layer similarity in multiplex networks: a systematic study. Royal Society Open Science 2018 5 171747, 10.1098/rsos.171747.
    PDF Code at CRAN
  19. Belfin R.V., Kanaga E F.M., Bródka P. (2018). Overlapping community detection using superior seed set selection in social networks. Computers & Electrical Engineering, 10.1016/j.compeleceng.2018.03.012
    PDF
  20. Erlandsson F., Bródka P., Boldt M., Johnson H. (2017). Do we really need to catch them all? A new User-guided Social Media Crawling method. Entropy, 19(12), 686.
    PDF Data at Harvard Dataverse
  21. Jankowski, J., Michalski, R., Bródka, P. (2017). A multilayer network dataset of interaction and influence spreading in a virtual world. Scientific Data, 4, Article number: 170144
    PDF Data at Harvard Dataverse
  22. Ethier, JF, Curcin, V., McGilchrist, M., Lim Choi Keung, S., Zhao, L., Andreasson, A., Bródka, P., Michalski, R., Arvanitis T., Mastellos, N., Burgun, A., Delaney B. (2017). eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial, International Journal of Medical Informatics, Volume 106, pp. 17-24, Elsevier
    PDF
  23. 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, 7(1), 891, DOI: 10.1038/s41598-017-00937-8
    PDF Presentation
  24. Jankowski J., Bródka P., Humari J. (2016). A Picture is Worth a Thousand Words: An Empirical Study on the Influence of Content Visibility on Diffusion Processes within a Virtual World. Behaviour & Information Technology, 35(11), 926-945.
    PDF
  25. Jankowski, J., Saganowski, S., Bródka, P. (2016). Evaluation of TRANSFoRm mobile eHealth solution for remote patient monitoring during clinical trials. Mobile Information Systems, 2016, Article ID 1029368
    PDF
  26. Erlandsson F., Bródka P., Borg A., Johnson H. (2016). Finding Influential Users in Social Media Using Association Rule Learning. Entropy Special Issue "Machine Learning and Entropy: Discover Unknown Unknowns in Complex Data Sets" 18(5), 164
    PDF
  27. Mastellos N., Bliźniuk G., Czopnik D., McGilchrist M., Misiaszek A., Bródka P., Curcin V., Car J., Delaney B., Andreasson A. (2016). Feasibility and acceptability of TRANSFoRm to improve clinical trial recruitment in primary care. Family Practice 33 (2): 186-191.doi: 10.1093/fampra/cmv102
    PDF
  28. Saganowski S., Bródka P., Misiaszek A., Fraczkowski K. (2015). Mobile eHealth solution (ePRO). In Journal of clinical bioinformatics (Vol. 5, No. 1, p. S14). BioMed Central.
    PDF
  29. Saganowski S., Gliwa B., Bródka P., Zygmunt A., Kazienko P., Koźlak J. (2015). Predicting Community Evolution in Social Networks. Entropy, 17(5), 3053-3096.
    PDF
  30. Różewski P., Jankowski J., Bródka P., Michalski R. (2015). Knowledge workers’ collaborative learning behavior modeling in an organizational social network. Computers in Human Behavior, 51, 1248-1260.
    arXiv:1505.03055
  31. Jankowski J., Michalski R., Bródka P., Kazienko P., Utz S. (2015). Knowledge Acquisition from Social Platforms Based on Network Distributions Fitting. Computers in Human Behavior, 51, 685-693.
    arXiv:1505.03049
  32. Musial K., Bródka P., Kazienko P., Gaworecki J. (2014). Extraction of Multi-layered Social Networks from Activity Data.The Scientific World Journal 2014, Article ID 359868, 13 pages.
    PDF
  33. Michalski R., Kajdanowicz T., Bródka P., Kazienko P. (2014). Seed Selection for Spread of Influnece in Social Networks: Temporal vs. Static Approach. New Generation Computing, 32(3-4), 213-235.
    arXiv.org:1405.0538
  34. Bródka P., Saganowski S., Kazienko P. (2013). GED: The Method for Group Evolution Discovery in Social Networks Social Network Analysis and Mining, 3(1), 1-14.
    arXiv.org:1207.4297 Code at GitHub
  35. Saganowski S., Bródka P., Kazienko P. (2012). Influence of the User Importance Measure on the Group Evolution Discovery. Foundations of Computing and Decision Sciences, 37(4), 293-303.
    arXiv.org:1301.1534
  36. Bródka P., Kazienko P., Musiał K., Skibicki K. (2012). Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks.International Journal of Computational Intelligence Systems, 5(3), 582-596.
    arXiv.org:1207.4293
  37. Zygmunt A., Bródka P., Kazienko P., Koźlak J. (2012). Key Person Analysis in Social Communities within Blogosphere. Journal of Universal Computer Science, 18(4), 577-597.
    JUCS18(4)
  38. Filipowski T., Kazienko P., Bródka P., Kajdanowicz T. (2012). Web-based knowledge exchange through social links in the workplace. Behaviour and Information Technology 31(8), 779-790.
  39. Kukla G., Kazienko P., Bródka P., Filipowski T. (2011). SocLaKE - Social Latent Knowledge Explorator, The Computer Journal, 55(3), 258-276.
  40. Palus S., Bródka P., Kazienko P. (2011). Evaluation of Organization Structure based on Email Interactions, International Journal of Knowledge Society Research, Vol. 2, No. 1, 2011, pp. 1-13.
  41. Kazienko P., Ruta D., Bródka P. (2009). The Impact of Customer Churn on Social Value Dynamics, International Journal of Virtual Communities and Social Networking (IJVCSN), 1(3), 62-74.

    42. Books and chapters

  42. Saganowski S., Bródka P., Kazienko P.: Community Evolution. Encyclopedia of Social Network Analysis and Mining pp 1-14 Springer New York 2017, doi 10.1007/978-1-4614-7163-9_223-1
    PDF
  43. Bródka P., Kazienko P.: Multi-layered social networks. Encyclopedia of Social Network Analysis and Mining. Volume 2. Springer, 2014, pp. 998-1013
    PDF
  44. Bródka P., Saganowski S., Kazienko P.: Community Evolution. Encyclopedia of Social Network Analysis and Mining. Volume 1. Springer, 2014, pp. 220-232.
    PDF
  45. Bródka P. A Method for Group Extraction and Analysis in Multilayer Social Networks Ph.D. disertation, Wrocław, Poland, 2012
    PDF
  46. Bródka P. Key Users in Social Network. How to find them? LAP Lambert Academic Publishing, 2012, ISBN-13: 978-3-659-19597-6, ISBN-10: 3659195979
    PDF
  47. Filipowski T., Bródka P., Kazienko P.: EcoRide - the social-based system for car traffic optimization. Chapter in Green Technologies and Business Practices: An IT Approach. IGI-Global,August 2012, DOI: 10.4018/978-1-4666-1972-2

    48. International Conferences

  48. Serwata, D., Nurek, M., & Michalski, R. (2024). A Perspective on the Ubiquity of Interaction Streams in Human Realm. In International Conference on Computational Science (pp. 353-367). Cham: Springer Nature Switzerland.
    PDF
  49. Paradowski, M. B., Bródka, P., Czuba, M., & Whitby, N. (2024). Dynamic computational social network analysis of language learner interactions: Novel insights for study-abroad second language acquisition. Society for Computation in Linguistics 7(1), 331–332. doi: https://doi.org/10.7275/scil.2218
    PDF
  50. Stępień, S., Jankowski, J., Bródka, P., & Michalski, R. (2023). The Role of Conformity in Opinion Dynamics Modelling with Multiple Social Circles. In International Conference on Computational Science (pp. 33-47). Cham: Springer Nature Switzerland.
  51. Sadaf, A., Mathieson, L., Bródka, P., & Musial, K. (2022). Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds. SIMBig 2022 International Conference on Information Management and Big Data
    PDF
  52. Czuba, M., Bródka, P. (2022). Network Diffusion: A Package to Simulate Spreading of Multiple Interacting Processes in Complex Networks. IEEE DSAA 2022 The 9th IEEE International Conference on Data Science and Advanced Analytics.
    PDF Main repository with code Runnable Code Ocean capsule Presentation
  53. Zioło, M., Bródka, P., Spoz, A., & Jankowski, J. (2022). Impact of external influence on green behavior spreading in multilayer network. IEEE DSAA 2022The 9th IEEE International Conference on Data Science and Advanced Analytics.
    PDF Presentation
  54. Belfin, R. V., Kanaga, E. G. M., Bródka, P. (2018, December). Regional clustering of Indian hospitals for better health. In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) (pp. 1-5). IEEE.
  55. Erlandsson F., Bródka P., Borg A.: Seed selection for information cascade in multilayer network The 6th International Conference on Complex Networks and Their Applications November 29 - December 01 2017 Lyon, France
    PDF
  56. Jankowski, J., Bródka, P., Michalski, R., Kazienko, P.: Seeds Buffering for Information Spreading Processes. SocInfo 2017, 9th International Conference on Social Informatics, Lecture Notes in Computer Science LNCS, vol. 10539, pp. 628-641, Springer, Berlin Heidelberg (2017)
    PDF
  57. Jankowski, J., Michalski, R., Bródka, P., Karczmarczyk, A. Increasing Coverage of Information Diffusion Processes by Reducing the Number of Initial Seeds. 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia, 31 July - 03 August, 2017
    PDF
  58. Jankowski, J., Bródka, P., Kazienko, P., Szymański, B., Kajdanowicz, T., Michalski, R. Sequential Seeding in Complex Networks - Trading Speed for Coverage. Poster at NetSci-X 2017, The 2017 International School and Conference on Network Science (2017)
  59. Erlandsson F., Bródka P., Borg A., Johnson H.: Finding Influential Users in Social Media Using Rule Learning Poster at Second International Conference on Computational Social Science IC2S2, Northwestern University, June 2016
  60. Saganowski S., Misiaszek A., Bródka P., Andreasson A., Curcin V., Delaney B., Frączkowski K.: TRANSFoRm eHealth solution for quality of life monitoring AMIA 2016 Summit on Clinical Research Informatics, CRI 2016, March 21-24, 2016, 2016, San Francisco, CA, pp. 231-239
  61. Erlandsson F., Borg A., Johnson H., Bródka P.: Predicting User Participation in Social Media. Poster at NetSciX 2016 - International School and Conference on Network Science, January 11-13, 2016, Wroclaw, Poland, LNCS Advances in Network Science Volume 9564 pp 126-135
  62. Jankowski J., Michalski R., Kazienko P., Bródka P., Utz S.: Adaptive Survey Design Using Structural Characteristics of the Social Networkg. SocInfo 2015 - The 7th International Conference on Social Informatics, Beijing, China, December 9-12, 2015, Proceedings (Vol. 9471, p. 153). Springer.
  63. Michalski R., Kajdanowicz T., Bródka P., Piotr Bródka, Przemysław Kazienko Kazienko P.: Spread of Influence in Temporal Networks. IC2S2 2015 - INTERNATIONAL CONFERENCE ON COMPUTATIONAL SOCIAL SCIENCE, June 8-11, 2015, Helsinki, Finland
  64. Bródka P., Kazienko P.: Group Extraction in Multi-layered Social Network. NetSci 2015 - International School and Conference on Network Science, June 1-5, 2015, Zaragoza, Spain
  65. Lim Choi Keung S.N, Zhao L., Curcin V, Ethier J-F., Burgun A., McGilchrist M., Bródka P., Tuligłowicz W., Andreasson A., Delaney B.C., Arvanitis T.N.TRANSFoRm: Implementing a Learning Healthcare System in Europe through Embedding Clinical Research into Clinical Practice, ICSS2014 - 48th Annual Hawaii International Conference on System Sciences, IEEE Computer Society Press
    PDF
  66. Bródka P., Sobas M., Johnson H.: Profile Cloning Detection in Social Networks. The First European Network Intelligence Conference, September 29-30, 2014, Wroclaw, Poland, IEEE Computer Society, pp. 63-68
  67. Michalski, R., Jankowski J., Bródka P., Kazienko P.: The Same Network - Different Communities? The Multidimensional Study of Groups in the Cyberspace. SNAA 2014 - the 4th Workshop on Social Network Analysis in Applications at ASONAM 2014 - The 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining, Beijing, China, August 17-20, 2014, IEEE, 2014,
  68. Michalski, R., Kazienko P., Kajdanowicz T., Bródka P.: Data-driven Seed Selection for Spread of Influence in Temporal Social Networks. Workshop on Sociophysics at SigmaPhi 2014 - The International Conference on Statistical Physics 2014, Rhodes, Greece, July 7-11, 2014, Kaniadakis G., Scarfone A.M. (eds.), 2014, p. 75.
  69. Saganowski S., Gliwa B., Bródka P., Zygmunt A., Kazienko P., Kozlak J.: Predicting Community Evolution in Social Networks, poster. NetSci 2014 - International School and Conference on Network Science
  70. Michalski R., Kajdanowicz T., Bródka P., Kazienko P.: Seed Selection in Social Networks - Temporal Approach Benefits. TnetSphys'14 - Temporal Networks, Human Dynamics and Social Physics Symposium at NetSci 2014 - International School and Conference on Network Science
  71. Frączkowski K., Bródka P., Misiaszek A., Saganowski S., Kazienko P.: The mobile system supporting health safety of patients in the TRANSFoRm project. Workshop: CRI Solution Day
  72. Brodka P, Kazienko P, Andreasson A, Fraczkowski K, Misiaszek A, Saganowski S, Zhao L, Curcin V, Arvanitis T, Delaney B, Michalski R.: Remote Patient Health Condition Monitoring for Clinical Research. 19th WONCA Europe Conference, Lisbon, 3 July 2014.
  73. Curcin V, Arvanitis T, Ethier J-F, Fraczkowski K, Kazienko P, Brodka P, Andreasson A, Blizniuk G, Lim Choi Keung S, Zhao L, Misiaszek A, McGilchrist M, Burgun A, Delaney B. Semantic Approach to Achieving Interoperability between Clinical Care and Clinical Research. 19th WONCA Europe Conference, Lisbon, 3 July 2014.
  74. Curcin V., Arvanitis T.N., Brodka P., Corrigan D., Delaney B.: TRANSFoRm digital infrastructure: The architecture for The Learning Healthcare System in Europe AMIA 2014 Clinical Research Informatics Summit Panel
  75. Gliwa B., Bródka P., Zygmunt A., Saganowski S., Kazienko P., Koźlak J.: Different Approaches to Community Evolution Prediction in Blogosphere. SNAA 2013 at ASONAM 2013, IEEE Computer Society, 2013, pp. .
  76. Kazienko P., Kajdanowicz T., Michalski R., Bródka P.: From Data to Human Behaviour. SOCIETY 2013, IEEE Computer Society, 2013, pp. .
  77. Kajdanowicz T., Filipowski T, Kazienko P., Bródka P. Competence Region Modelling in Relational ClassificationACIIDS 2013, LNCS 7803, 2013, pp 236-244.
  78. Bródka P., Filipowski T., Kazienko P.: An Introduction to Community Detection in Multi-layered Social Network , WSKS 2011, CCIS 278, Springer, pp 185-190, 2012.
    arXiv.org:1209.6050
  79. Saganowski S., Bródka P., Kazienko P.: Influence of the Temporal Social Network Timeframe Type and Size on Tracking Group Evolution. Workshop on Temporal and Dynamic Networks: From Data to Models at NetSci 2013
  80. Michalski R., Jankowski J., Bródka P., Kazienko P.: How the Network Dynamics Influences the Diffusion of Innovations. Workshop on Temporal and Dynamic Networks: From Data to Models at NetSci 2013
  81. Bródka P., Kazienko P., Saganowski S., Michalski R.: Quantifying Multi-layered Complex Networks. Satellite Symposium on Multiple Network Modeling, Analysis and Mining at NetSci 2013
    82. Publications
  82. Bródka P., Kazienko P., Kołoszczyk B.: Predicting Group Evolution in the Social Network. SocInfo 2012, LNCS 7710, Springer, 2012, pp. 54–67.
    arXiv.org:1210.5161
  83. Michalski R., Bródka P., Kazienko P., Juszczyszyn K.: Quantifying Social Network Dynamics. CASoN 2012, IEEE Computer Society, 2012, pp. 69-74.
    arXiv.org:1303.5009
  84. Saganowski S., Bródka P., Kazienko P.: Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery. ASONAM 2012, IEEE Computer Society, 2012, pp. 678-682.
    arXiv.org:1210.5167
  85. Gliwa B., Saganowski S., Zygmunt A., Bródka P., Kazienko P., Koźlak J.: Identification of GroupChanges in Blogosphere. ASONAM 2012, IEEE Computer Society, 2012, pp. 1233-1238.
    arXiv.org:1210.5171
  86. Bródka P., Saganowski S., Kazienko P.: Group Evolution Discovery in Social Networks. NetSci 2012
  87. Bródka P., Saganowski S., Kazienko P.: Tracking Group Evolution in Social Networks. SocInfo 2011, Lecture Notes in Artificial Intelligence LNAI , Springer, 2011, pp. 316-319. Best Demo\Poster\Paper Award Fotografie 1, 2 i 3 z ceremoni wręczenia nagrody.
    arXiv.org:1210.5240
  88. Michalski R., Palus S., Kazienko P., Bródka P., Juszczyszyn K.: Modelling Social Network Evolution. SocInfo 2011, Lecture Notes in Artificial Intelligence LNAI , Springer, 2011, pp. 283-286
  89. Bródka P., Skibicki K., Kazienko P., Musiał K.: A Degree Centrality in Multi-layered Social Network. CASoN 2011, IEEE Computer Society, 2011, pp. 237-242
    arXiv.org:1210.5184
  90. Bródka P., Saganowski S., Kazienko P.: Group Evolution Discovery in Social Networks, ASONAM 2011, IEEE Computer Society, 2011, pp. 247-253
  91. Bródka P., Stawiak P., Kazienko P.: Shortest Path Discovery in the Multi-layered Social Network, ASONAM 2011, IEEE Computer Society, 2011, pp 497-501
    arXiv.org:1210.5180
  92. Zygmunt A., Bródka P., Kazienko P., Koźlak J.: Different Approaches to Groups and Key Person Identification in Blogosphere, SNAA 2011 at ASONAM 2011, IEEE Computer Society, 2011, pp. 593-598
  93. Kazienko P., Musial K., Kukla E., Kajdanowicz T., Bródka P.: Multidimensional Social Network: Model and Analysis, ICCCI 2011, Lecture Notes in Artificial Intelligence LNAI, Springer, 2011, pp. 378-387.
  94. Kazienko P., Kukla E., Musial K., Kajdanowicz T., Bródka P., Gaworecki J.: A Generic Model for Multidimensional Temporal Social Network, ICeND2011, CCIS 171, Springer, 2011, pp. 1-14
  95. Popowicz D., Bródka P., Kazienko P., Kozielski M.: Ask Your Friends for Help: A Collaborative Query Answering System, CDVE2011, Lecture Notes in Computer Science, Springer, 2011, pp. 110-113
  96. Kukla G., Kazienko P., Bródka P., Filipowski T.: Recommendation Boosted Query Propagation in the Social Network, SocInfo 2010, Lecture Notes in Artificial Intelligence LNAI , Springer, 2010, pp. 113-124
  97. Kazienko P., Bródka P., Musial K., Gaworecki J.: Multi-layered Social Network Creation Based on Bibliographic Data , SocialCom2010, IEEE Computer Society Press, 2010, pp. 407-412
  98. Juszczyszyn K., Musiał A., Musiał K., Bródka P.: Utilizing Dynamic Molecular Modelling Technique for Predicting Changes in Complex Social Networks, 2010 IEEE/WIC/ACM, IEEE Computer Society Press, 2010, pp. 1-4
  99. Kazienko P., Bródka P., Musiał K.: Individual Neighbourhood Exploration in Complex Multi-layered Social Network, 2010 IEEE/WIC/ACM, IEEE Computer Society Press, 2010, pp. 5-8
  100. Bródka P., Musial K., Kazienko P.: A Method for Group Extraction in Complex Social Networks., WSKS 2010, CCIS 111, Springer, 2010, pp. 238-247.
  101. Palus S., Bródka P., Kazienko P.: How to Analyze Company Using Social Network?, WSKS 2010, CCIS 111, Springer, 2010, pp. 159-164.
  102. Bródka P., Musiał K., Kazienko P.: Efficiency of Node Position Calculation in Social Networks, KES 2009, LNCS/LNAI, Springer, 2009, pp.455-463
  103. Bródka P., Musiał K., Kazienko P.: A Performance of Centrality Calculation in Social Networks, CASoN 2009, IEEE Computer Society, 2009, pp.24-31.
  104. Musiał K., Kazienko P., Bródka P.:User position measures in social networks SNA-KDD 2009
  105. Ruta D., Kazienko P., Bródka P.: Network-Aware Customer Value in Telecommunication Social Networks, ICAI 09, CSREA Press, pp. 261-267.
  106. Kazienko P., Bródka P., Ruta D.: The Influence of Customer Churn and Acquisition on Value Dynamics of Social Neighbourhoods, WSKS 2009, LNCS/LNAI, Springer, 2009, pp.491-500
  107. Juszczyszyn K., Musiał A., Musiał K., Bródka P.: Molecular Dynamics Modelling of the Temporal Changes in Complex Networks, CEC 09, IEEE Computer Society Press, 2009 pp. 553-559.

Projects

  1. MultiSpred - Control and spreading processes in multilayer networks, National Science Centre, Poland, 2023-26
  2. OMINO - Overcoming Multilevel INformation Overload, HORIZON-MSCA-2021-SE-01, 2023-26
  3. Modelling social interactions using data streams, National Science Centre, Poland, 2022-25.
  4. V4 CARE ARSEC - Green Energy Transition, Post-COVID Central Europe & the Green Transition AI-Assisted Impact Analysis, Projections & Scenarios, International Visegrad Fund, 2022-24
  5. MultiSpred - Spread of influence in multilayer networks, National Science Centre, Poland, 2017-22
  6. V4 CARE ARSEC - Computer Aided REasoning in Analysing Regional Socio-Economic Complexity, International Visegrad Fund, 2018-20
  7. RENOIR - Reverse EngiNeering of sOcial Information pRocessing, H2020-MSCA-RISE-2015, 2016-19, no. 691152.
  8. ENGINE - European research centre of Network intelliGence for INnovation Enhancement, FP7-REGPOT-2012-2013-1, 2013-16, no. 316097

Code

  1. Network Diffusion Python package
    Paper, Main repository, PyPi package, Full documentation, Anaconda package, Examples, CodeOcean capsule
  2. Replication code for Stolarski M, Piróg A, Bródka P. (2024). Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach. Entropy, 2024; 26(11):955. https://doi.org/10.3390/e26110955.
    Code at GitHub
  3. Replication code and data for Paradowski, M. B., Whitby, N., Czuba, M., & Bródka, P. (2024). Peer Interaction Dynamics and Second Language Learning Trajectories During Study Abroad: A Longitudinal Investigation Using Dynamic Computational Social Network Analysis. Language Learning.
    Repository with code and data
  4. Replication code for Bródka, P. Jankowski, J., & Michalski, R. (2021). Sequential seeding in multilayer networks. Chaos 31, 033130; https://doi.org/10.1063/5.0023427
    Code and data at GitHub and Code Ocean
  5. Replication code for Michalski, R., Jankowski, J. & Bródka, P. (2020). Effective Influence Spreading in Temporal Networks with Sequential Seeding. IEEE Access, vol. 8, pp. 151208-151218, doi: 10.1109/ACCESS.2020.3016913.
    Code and data at Code Ocean
  6. GED - The Method for Group Evolution Discovery in Social Networks. Bródka P., Saganowski S., Kazienko P.: GED: The Method for Group Evolution Discovery in Social Networks, Social Network Analysis and Mining, March 2013, Volume 3, Issue 1, pp 1-14, DOI:10.1007/s13278-012-0058-8
    Paper Code
  7. mLFR Benchamark - this is the extention of LFR Benchmark for multilayer networks, which enables researchers to test and compare community detection algorithms in multilayer, multiplex and multiple social networks. Bródka P. A Method for GroupExtraction and Analysis in Multi-layered Social Networks Ph.D. disertation, Wrocław, Poland, 2012
    Paper Code

Data

  1. Jankowski, J., Michalski, R., Bródka, P. (2017). A multilayer network dataset of interaction and influence spreading in a virtual world. Scientific Data, 4, Article number: 170144, Data at Harvard Dataverse.
    Presented data contains the record of five spreading campaigns that occurred in a virtual world platform. During these campaigns, users were distributing the avatars between each other. The processes were either incentivized or not incentivized, and varying in time and range. The campaign data is accompanied by the events that can be used to build a multilayer network in order to be able to place these campaigns in a wider context (friendships, messages, transactions, etc.).

  2. Nurek, M., & Michalski, R. (2020). Combining machine learning and social network analysis to reveal the organizational structures. Applied Sciences, 10(5), 1699. Data at Harvard Dataverse.
    History of internal e-mail communication (sender, recipient, datetime) between employees of a mid-sized manufacturing company. Multiple recipients of the same e-mail (To, CC, BCC) are represented as separate rows without distinguishing the recipient type. In this version apart from the communication metadata the organizational structure of the company is published (who reports to whom). The period covered are nine full months of 2010 starting from 2010-01-01 to 2010-09-30 (event dates in local time).

  3. Michalski, R., Dziubałtowska, D., & Macek, P. (2020). Revealing the character of nodes in a blockchain with supervised learning. IEEE Access, 8, 109639-109647. Data at Harvard Dataverse.
    The dataset contains Bitcoin addresses that have been identified and belong to one of particular categories: mining pools, miners, coinjoin services, gambling services, exchanges, other services - 8,008 addresses in total. The assignment of labels comes from two sources: plausible assumptions and external services and is not guaranteed to be error prone. These labels have been used for training and validating the performance of machine learning algorithms for discovering the types of addresses.

Video

  1. Network Diffusion: A Package to Simulate Spreading of Multiple Interacting Processes in Complex Networks, YouTube
  2. Impact of external influence on green behavior spreading in multilayer network, YouTube
  3. Using sequential seeding for influence maximization in social networks, YouTube
  4. Sequential seeding in multilayer networks, YouTube

Let's Get In Touch!

Would you like to colaborate with us?

+48 0 71 320 3453
piotr.brodka@pwr.edu.pl radoslaw.michalski@pwr.edu.pl