Name
Abstract | ||
|---|---|---|
In this paper we study the identifiability of users across social networks, with a trainable combination of different similarity metrics. This application is becoming particularly interesting as the number and variety of social networks increase and the presence of individuals in multiple networks is becoming commonplace. Motivated by the need to verify information that appears in social networks, as addressed by the research project REVEAL, the presence of individuals in different networks provides an interesting opportunity: we can use information from one network to verify information that appears in another. In order to achieve this, we need to identify users across networks. We approach this problem by a combination of similarity measures that take into account the users' affiliation, location, professional interests and past experience, as stated in the different networks. We experimented with a variety of combination approaches, ranging from simple averaging to trained hybrid models. Our experiments show that, under certain conditions, identification is possible with sufficiently high accuracy to support the goal of verification. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24261-3_14 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
User identification,Similarity learning,Entity resolution | Similarity learning,Name resolution,Social network,Identifiability,Computer science,Ranging,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
9370 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Katerina Zamani | 1 | 0 | 0.34 |
| Georgios Paliouras | 2 | 1510 | 120.93 |
| Dimitrios Vogiatzis | 3 | 6 | 1.09 |