Przemysław Kazienko
Wrocław University of Technology
257 Papers
1.6K Citations
Przemysław Kazienko is an academic researcher from Wrocław University of Technology. The author has contributed to research in topics: Social network & Computer science. The author has an hindex of 33, co-authored 236 publications. Previous affiliations of Przemysław Kazienko include University of Wrocław.
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Papers
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Priority Attachment: a Universal Mechanism for Generating Networks.
TL;DR: It is shown that by using priority attachment the authors can generate networks of very diverse topologies, as well as recreate empirical networks, by using a simple network generative model based on the priority attachment mechanism.
Current Trends and Difficulties in Knowledge-Based e-Health Systems
Katarzyna Ewa Pasierb,Tomasz Kajdanowicz,Przemysław Kazienko +2 more
- 21 Sep 2011
TL;DR: A discussion on matters arising from encountered problems while designing and introducing e-health systems is presented, and the future vision of healthcare evolution by means of information technology is analysed.
Incremental Learning in Dynamic Networks for Node Classification
Tomasz Kajdanowicz,Kamil Tagowski,Maciej Falkiewicz,Przemysław Kazienko +3 more
- 11 Sep 2017
TL;DR: An incremental learning method for nodes’ classification that varies over time and depends on information spread in the network is proposed and demonstrated in experiments with real data set, showing that the method can effectively classify the future state of nodes.
Competence region modelling in relational classification
Tomasz Kajdanowicz,Tomasz Filipowski,Przemysław Kazienko,Piotr Bródka +3 more
- 18 Mar 2013
TL;DR: Preliminary results obtained from experiments performed on real world datasets competence region modelling approach to relational classification results with more accurate classification than standard approach.
Extracting Aspects Hierarchies using Rhetorical Structure Theory
TL;DR: In this paper, an unsupervised technique using Rhetorical Structure Theory and graph analysis was proposed to generate aspect hierarchies that proved to be consistently correct compared with human-generated hierarchies.