Predicting Community Evolution in Social Networks
Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-05-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/17/5/3053 |
| _version_ | 1811184269635092480 |
|---|---|
| author | Stanisław Saganowski Bogdan Gliwa Piotr Bródka Anna Zygmunt Przemysław Kazienko Jarosław Koźlak |
| author_facet | Stanisław Saganowski Bogdan Gliwa Piotr Bródka Anna Zygmunt Przemysław Kazienko Jarosław Koźlak |
| author_sort | Stanisław Saganowski |
| collection | DOAJ |
| description | Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods. |
| first_indexed | 2024-04-11T13:10:43Z |
| format | Article |
| id | doaj.art-b791fb8e4b184f388006e71b5059553f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:10:43Z |
| publishDate | 2015-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b791fb8e4b184f388006e71b5059553f2022-12-22T04:22:36ZengMDPI AGEntropy1099-43002015-05-011753053309610.3390/e17053053e17053053Predicting Community Evolution in Social NetworksStanisław Saganowski0Bogdan Gliwa1Piotr Bródka2Anna Zygmunt3Przemysław Kazienko4Jarosław Koźlak5Department of Computational Intelligence, Wrocław University of Technology, Wyb.Wyspiańskiego 27, 50-370 Wrocław, PolandAGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, PolandDepartment of Computational Intelligence, Wrocław University of Technology, Wyb.Wyspiańskiego 27, 50-370 Wrocław, PolandAGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, PolandDepartment of Computational Intelligence, Wrocław University of Technology, Wyb.Wyspiańskiego 27, 50-370 Wrocław, PolandAGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, PolandNowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI) and Group Evolution Discovery (GED). Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods.http://www.mdpi.com/1099-4300/17/5/3053social networksocial network analysis (SNA)social communitysocial group detectiongroup evolutiongroup evolution predictiongroup dynamicsclassifierfeature selectionGEDSGCI |
| spellingShingle | Stanisław Saganowski Bogdan Gliwa Piotr Bródka Anna Zygmunt Przemysław Kazienko Jarosław Koźlak Predicting Community Evolution in Social Networks Entropy social network social network analysis (SNA) social community social group detection group evolution group evolution prediction group dynamics classifier feature selection GED SGCI |
| title | Predicting Community Evolution in Social Networks |
| title_full | Predicting Community Evolution in Social Networks |
| title_fullStr | Predicting Community Evolution in Social Networks |
| title_full_unstemmed | Predicting Community Evolution in Social Networks |
| title_short | Predicting Community Evolution in Social Networks |
| title_sort | predicting community evolution in social networks |
| topic | social network social network analysis (SNA) social community social group detection group evolution group evolution prediction group dynamics classifier feature selection GED SGCI |
| url | http://www.mdpi.com/1099-4300/17/5/3053 |
| work_keys_str_mv | AT stanisławsaganowski predictingcommunityevolutioninsocialnetworks AT bogdangliwa predictingcommunityevolutioninsocialnetworks AT piotrbrodka predictingcommunityevolutioninsocialnetworks AT annazygmunt predictingcommunityevolutioninsocialnetworks AT przemysławkazienko predictingcommunityevolutioninsocialnetworks AT jarosławkozlak predictingcommunityevolutioninsocialnetworks |