Research Interests: Social Computing, Recommender Systems, Complex Networks, Web Science, Open Science, Data Mining, Machine Learning

About: Ass.-Prof. Dr. Elisabeth Lex is assistant professor at Graz University of Technology and she heads the Social Computing research area at Know-Center. Her research interests include Social Computing, Recommender Systems, Complex Networks, Open Science, Web Science and Machine Learning. Elisabeth has been work package leader in the FP7 IP Learning Layers, and scientific coordinator of the Marie Curie IRSES Web Information Quality Evaluation Initiative (WIQ-EI) project as well as task leader in the H2020 Analytics for Everyday Learning (AFEL) project. In AFEL, she researches on novel recommender systems and on opinion dynamics in online collaboration networks. Her group leads a work package in the H2020 CSA OpenUp project with focus on how to disseminate research results to a broader audience. Elisabeth has been member of the Expert Group on Altmetrics, which advised the European Commission, DG Research and Innovation. The expert group developed policies for the commission on how to use altmetrics to assess the impact of scientific artefacts. Elisabeth has published more than 60 scientific publications in venues such as the ACM World Wide Web Conference (WWW), ACM Conference on Hypertext and Social Media (HT), ACM Conference on Recommender Systems, as well as in journals such as Scientometrics, Frontiers in Resarch Metrics & Analytics and the International Journal of Human–Computer Interaction on topics such as Recommender Systems, Social Network Analysis, Altmetrics, Data Mining, and Machine Learning and she has given several invited talks in the mentioned fields. Elisabeth regularly acts as Senior PC member, PC member and co-organizes and co-chairs a number of workshops and conferences at venues such as ACM Web Science or OpenSym. Among other courses at Graz University of Technology, Elisabeth teaches Web Science and she will start a new course on Complex Systems in 2018.

Grants, Fellowships and Awards:

  • 10/2017-02/2018 Erasmus+ Staff Mobility Grant. I will be doing research at the Chair for Computational Social Sciences and Humanties at RWTH Aachen in the group of Prof. Markus Strohmaier on biases in recommender systems
  • 10/2015 Demo Honorable Mention Award 15th International Conference on Knowledge Technologies and Data Driven Business (i-KNOW’15)
  • 09/2015 Special Mention Third Demo Award at the Tenth Conference on Technology Enhanced Learning (EC-TEL’15)
  • 06/2015 Outstanding Paper Award 15th International Society of Scientometrics and Informetrics Conference (ISSI’15)
  • 12/2012 Industrial Research Prize (Universitätsforschungspreis der Industrie 2012), 2. Place
  • 10/2011-01/2012 Marie Curie IRSES fellowship (visiting researcher at UNSL Argentina)
  • 09/2010 Runner Up at ECML/PKDD Discovery Challenge 2010
  • 05/2010 SIGWEB Student Travel Award for Hypertext 2010
  • 2009 Scholarship: Career Program for female Scientists, Karl-Franzens-Universität Graz, Austria

Selected Publications (see full list under Publications):

  • Hasani-Mavriqi, Kowald, D., Helic, D., Lex, E. Consensus Dynamics in Online Collaboration Systems. Accepted for publication in Computational Social Networks, Springer. 2018
  • Kowald, D., Pujari, S., Lex, E. Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach. In Proceedings of the 26th International World Wide Web Conference (WWW’2017). ACM.
  • Kopeinik, S., Kowald, D., Hasani-Mavriqi, I., Lex, E. (2017) Improving Collaborative Filtering Using a Cognitive Model of Human Category Learning. In The Journal of Web Science, Vol. 2, No. 1
  • Hasani-Mavriqi, I., Geigl, F., Pujari, S.C., Lex, E., Helic, D. The influence of social status and network structure on consensus building in collaboration networks. In Social Network Analysis and Mining 6 (1), 80 (SNAM). 2016. doi:10.1007/s13278-016-0389-y
  • Kowald, D., Lex, E. The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems. In Proceedings of the 27th ACM Conference on Hypertext and Hypermedia (HT2016)
  • Peters, I., Kraker, P., Lex, E., Gumpenberger, C., and Gorraiz, J. Research data explored: an extended analysis of citations and altmetrics. Scientometrics, 2016.  http://dx.doi.org/10.1007/s11192-016-1887-4
  • Kowald, D. and Lex, E. Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study. In Proceedings of the 9th ACM Recommender Systems Conference (RecSys 2015)
  • Seitlinger, P., Kowald, D., Kopeinik, S., Hasani-Mavriqi, I., Ley, T., and Lex, E. (2015) Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics. In 24rd International World Wide Web Conference (WWW2015), Web-Science Track, Companion Volume, New York: ACM.

News and Events:

Want to collaborate? Contact me! elisabeth dot lex at tugraz dot at

If you are interested in collaboration, and/or in doing a project and/or thesis (bachelor, master) with me, feel free to contact me. If your interests don’t meet the suggested topics below but are related to my research, contact me as well and we’ll try to come up with a topic for your thesis.

Open master thesis topics 

  • Implementing a Recommender System Using a Cognitive Model on the Web: The aim of this thesis is to implement a computational cognitive model that explains  behavior of users on the Web and to deploy this model for recommender systems. The model will be evaluated on well-known datasets from the research community and will be compared with other state-of-the art approaches. The algorithm should be implemented in Java or Python and there is the possibility to publish the results of the thesis in form of an academic paper together with the supervisors.
  • Implementation of Social Learning Analytics: Social learning analytics make use of data generated by learners’ online activity in order to identify behaviors and patterns within the learning environment. The goal of this thesis is to collect data about learners and their contexts from the Social Web (e.g. StackExchange, Reddit) and to analyze this data using Social Network Analysis (e.g. using graph-tool). The focus is on studying direct interactions (e.g. messaging, friending, following) and/or indirect interactions, which happen when learners leave behind activity traces such as e.g. ratings or tags.
  • Influencer Detection in Academic Graphs: The aim of this thesis is to work with the publicly available Microsoft Academic Graph dataset that includes information about academic publications and citations. The main focus is to study influential nodes of various types including authors, affiliations and venues. One concrete idea would be to identify the best research institutions based on their publications and how they are cited in research articles. The realisation of other ideas is also possible.
  • Opinion Dynamics in Social Networks: The aim of this thesis is to build on our existing work, in which we studied the impact of social status on Opinion Dynamics and consensus building in Online collaboration networks. See http://www.know-center.tugraz.at/cms/wp-content/uploads/2015/08/ASONAM_2015_Paper.pdf for details. Specifically, within this thesis, the existing analysis should be extended to other types of networks. For the thesis, our open source framework for Opinion Dynamics can be used (Python, graph-tool) and extended. In case of relevant findings, there is the possibility to publish the results of the thesis in form of an academic paper together with the supervisors of the thesis.

Supervised theses

  • Tomislav Duricic (PhD thesis ongoing since 2016, expected to finish 2019). Behavior and Dynamics of Social Media and their impact on Cryptocurrency Markets (co-supervision with Univ. Prof. Stefanie Lindstaedt)
  • Ilire Hasani-Mavriqi (PhD thesis ongoing since 2015, expected to finish 2018). Influence of Status on Consensus Building in Social Networks (co-supervision with Assoc. Prof. Denis Helic)
  • Emanuel Lacic (PhD thesis ongoing since 2014, expected to finish 2018). Tackling Real-World Requirements in Recommender Systems (co-supervision with Univ. Prof. Stefanie Lindstaedt)
  • Simone Kopeinik (2017). Applying Cognitive Learner Models for Recommender Systems in Small-Scale Learning Environments (PhD thesis, finished. Co-supervision with Univ. Prof. Stefanie Lindstaedt)
  • Dominik Kowald (2017). Modeling Activation Processes in Human Memory to Improve Tag Recommendations  (PhD thesis, finished. Co-supervision with Univ. Prof. Stefanie Lindstaedt)
  • Wagner, M. (Master thesis ongoing since 2017, expected to finish 2018). Mitigating bias and polarisation in social networks through recommendations.
  • Punz, A. (2016). Detection and Analysis of Communities on Twitter (master thesis, finished)
  • Müllner, P. (2016). Werkzeuge zur Analyse von Lerngruppen anhand derer Aktivitäten in einem sozialen Netzwerk (bachelor thesis, finished)
  • Steinkellner, C. (2016). Empirical Analysis of Social Networks at a Scientific Conference (master thesis, finished)
  • Feichtner, V. (2016). Information Quality in Wikipedia (master thesis, finished)
  • Feichtner, V. (2016) Processing Large Edit Networks in Wikipedia (master project, finished)
  • Duricic, T. (2015) Real-time recommendations using trust metrics and NoSQL databases, 2015. Cooperation via Erasmus+ programme together with University of Technology Zagreb, Croatia (master thesis, finished)
  • Steinkellner, C. (2015). Empirical Analysis of Social Networks at a Scientific Conference (master project, finished)