Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks
The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/5/214 |
| _version_ | 1827692236833292288 |
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| author | Pokpong Songmuang Chainarong Sirisup Aroonwan Suebsriwichai |
| author_facet | Pokpong Songmuang Chainarong Sirisup Aroonwan Suebsriwichai |
| author_sort | Pokpong Songmuang |
| collection | DOAJ |
| description | The current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source. |
| first_indexed | 2024-03-10T11:17:32Z |
| format | Article |
| id | doaj.art-deb74c95037b444c90045754cd8e1d9f |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T11:17:32Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-deb74c95037b444c90045754cd8e1d9f2023-11-21T20:18:40ZengMDPI AGInformation2078-24892021-05-0112521410.3390/info12050214Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social NetworksPokpong Songmuang0Chainarong Sirisup1Aroonwan Suebsriwichai2Faculty of Science and Technology, Thammasat University, Pathumthani 12121, ThailandFaculty of Science and Technology, Thammasat University, Pathumthani 12121, ThailandFaculty of Science and Technology, Thammasat University, Pathumthani 12121, ThailandThe current methods for missing link prediction in social networks focus on using data from overlapping users from two social network sources to recommend links between unconnected users. To improve prediction of the missing link, this paper presents the use of information from non-overlapping users as additional features in training a prediction model using a machine-learning approach. The proposed features are designed to use together with the common features as extra features to help in tuning up for a better classification model. The social network data sources used in this paper are Twitter and Facebook where Twitter is a main data for prediction and Facebook is a supporting data. For evaluations, a comparison using different machine-learning techniques, feature settings, and different network-density level of data source is studied. The experimental results can be concluded that the prediction model using a combination of the proposed features and the common features with Random Forest technique gained the best efficiency using percentage amount of recovering missing links and F1 score. The model of combined features yields higher percentage of recovering link by an average of 23.25% and the F1-measure by an average of 19.80% than the baseline of multi-social network source.https://www.mdpi.com/2078-2489/12/5/214Social Networkmissing linklink predictionmachine learning |
| spellingShingle | Pokpong Songmuang Chainarong Sirisup Aroonwan Suebsriwichai Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks Information Social Network missing link link prediction machine learning |
| title | Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks |
| title_full | Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks |
| title_fullStr | Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks |
| title_full_unstemmed | Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks |
| title_short | Missing Link Prediction Using Non-Overlapped Features and Multiple Sources of Social Networks |
| title_sort | missing link prediction using non overlapped features and multiple sources of social networks |
| topic | Social Network missing link link prediction machine learning |
| url | https://www.mdpi.com/2078-2489/12/5/214 |
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