Link prediction for new users in Social Networks
| Autor: | Reza Farahbakhsh, Xiao Han, Noel Crespi, Son N. Han, Chao Chen, Leye Wang |
|---|---|
| Přispěvatelé: | Département Réseaux et Services Multimédia Mobiles (RS2M), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Centre National de la Recherche Scientifique (CNRS), Département Réseaux et Services de Télécommunications (RST), Chongqing University [Chongqing], Réseaux, Systèmes, Services, Sécurité (R3S-SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Social network
Relation (database) business.industry Computer science Sign (semiotics) Machine learning computer.software_genre Data set Support vector machine [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] Feature (machine learning) New-user link prediction Artificial intelligence Link (knot theory) business computer |
| Zdroj: | Proceedings ICC 2015 : IEEE International Conference on Communications ICC 2015 : IEEE International Conference on Communications ICC 2015 : IEEE International Conference on Communications, Jun 2015, London, United Kingdom. pp.1250-1255, ⟨10.1109/ICC.2015.7248494⟩ Publons ICC |
| DOI: | 10.1109/ICC.2015.7248494⟩ |
| Popis: | International audience; Link prediction for new users who have not created any link is a fundamental problem in Online Social Networks (OSNs). It can be used to recommend friends for new users to start building their social networks. The existing studies use crossplatform approaches to predict a new user's links on a certain OSN by porting his existing links from other OSNs. However, it cannot work when OSNs are not willing to share their data or users do not want to connect different OSN accounts. In this paper, we use a single-platform approach to carry out the link prediction. We explore the users' profile attributes (e.g., workplace, high school and hometown) which can be easily obtained during the new users' sign up procedure. Based on the limited available information from the new user, along with the attributes and links from existing users, we extract three types of social features: basic feature, derived feature and latent relation feature. We propose a link prediction model using these social features based on Support Vector Machines. Eventually, we rely on a large Facebook data set consisting of 479; 000 users to evaluate our proposed model. The result reveals that our model outperforms the baselines by achieving the AUC value of 0:83; it also demonstrates that each of the proposed social features contribute significantly to the prediction model |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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