Name
Abstract | ||
|---|---|---|
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 multi-stage 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1371/journal.pone.0224194 | PLOS ONE |
Field | DocType | Volume |
Training set,Social group,Social network,Social community,Computer science,Transfer of learning,Complex network,Artificial intelligence,Classifier (linguistics),Machine learning | Journal | 14 |
Issue | ISSN | Citations |
10 | 1932-6203 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanislaw Saganowski | 1 | 121 | 10.75 |
| Piotr Bródka | 2 | 297 | 27.05 |
| Michal Koziarski | 3 | 33 | 4.18 |
| Przemyslaw Kazienko | 4 | 640 | 63.34 |