Analysis of group evolution prediction in complex networks

Autoři: Stanisław Saganowski aff001;  Piotr Bródka aff001;  Michał Koziarski aff002;  Przemysław Kazienko aff001
Působiště autorů: Department of Computational Intelligence, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland aff001;  Department of Electronics, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Kraków, Poland aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article


In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict the evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic, and multistage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well.

Klíčová slova:

Community structure – Decision trees – Evolutionary immunology – Facebook – Machine learning – Network analysis – Social networks – Viral evolution


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2019 Číslo 10
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