Name
Abstract | ||
|---|---|---|
Abundance of online social platforms allows users to create more than one profile in different social networks. Several issues arise due to multiple user identities including data integration and information retrieval in social networks. Identifying user profiles across social platforms is known as entity resolution. In this paper, we propose a degree-based method to attack entity resolution problems. More precisely, we utilize the degree of users and their friends to identify user profiles. Our results show that, without help of critical information such as e-mail addresses, the proposed method can outperform existing string matching-based solutions as well as popular classifiers such as SVM and Naive Bayes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s13278-018-0499-9 | Social Netw. Analys. Mining |
Keywords | Field | DocType |
Entity resolution,User profile matching,String matching,Social network analysis | Data integration,String searching algorithm,Name resolution,Social network,Naive Bayes classifier,Computer science,Social network analysis,Support vector machine,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
8 | 1 | 1869-5450 |
Citations | PageRank | References |
1 | 0.36 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jooyoung Lee | 1 | 573 | 46.13 |
| Rasheed Hussain | 2 | 236 | 31.12 |
| Víctor Rivera | 3 | 52 | 12.94 |
| Davlatbek Isroilov | 4 | 1 | 0.36 |