most citedMultidimensional Social Network in the Social Recommender System

201 citations · 281 across the 11 of their papers we have counts for

collaborators

11 papers

cs.SI2013

Group Evolution Discovery in Social Networks

Piotr Bródka, Stanisław Saganowski, Przemysław Kazienko

Group extraction and their evolution are among the topics which arouse the greatest interest in the domain of social network analysis. However, while the grouping methods in social…

cs.DB20132 cited

Privacy-preserving Data Mining, Sharing and Publishing

Katarzyna Pasierb, Tomasz Kajdanowicz, Przemyslaw Kazienko

The goal of the paper is to present different approaches to privacy-preserving data sharing and publishing in the context of e-health care systems. In particular, the literature re…

cs.SI20135 cited

Quantifying Social Network Dynamics

Radosław Michalski, Piotr Bródka, Przemysław Kazienko +1

The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics. The following paper focuses on the def…

cs.SI201312 cited

Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks

Radosław Michalski, Jarosław Jankowski, Przemysław Kazienko

Viral campaigns are crucial methods for word-of-mouth marketing in social communities. The goal of these campaigns is to encourage people for activity. The problem of incentivised…

cs.SI2013201 cited

Multidimensional Social Network in the Social Recommender System

Przemyslaw Kazienko, Katarzyna Musial, Tomasz Kajdanowicz

All online sharing systems gather data that reflects users' collective behaviour and their shared activities. This data can be used to extract different kinds of relationships, whi…

cs.SI20135 cited

Influence Of The User Importance Measure On The Group Evolution Discovery

Stanisław Saganowski, Piotr Bródka, Przemysław Kazienko

One of the most interesting topics in social network science are social groups. Their extraction, dynamics and evolution. One year ago the method for group evolution discovery (GED…