The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Finding Similar Users in Facebook
Abstract
Online social networks are rapidly asserting themselves as popular services on the Web. A central point is to determine whether two distinct users can be considered similar, a crucial concept with interesting consequences on the possibility to accomplish targeted actions like, for example, political and social aggregations or commercial promotions. In this chapter, the authors propose an approach in order to estimate the similarity of two users based on the knowledge of social ties (i.e., common friends and groups of users) existing among users, and the analysis of activities (i.e., social events) in which users are involved. For each of these indicators, authors draw a local measure of user similarity, which takes into account only their joint behaviours. After this, the chapter considers the whole network of relationships among users along with local values of similarities and combine them to obtain a global measure of similarity. Applying the Katz coefficient, a popular parameter introduced in Social Science research, carries out such a computation. Finally, similarity values produced for each social activity are merged into a unique value of similarity by applying linear regression.
Related Content
|
Nitesh Behare, Rashmi D. Mahajan, Meenakshi Singh, Vishwanathan Iyer, Ushmita Gupta, Pritesh P. Somani.
© 2024.
36 pages.
|
|
Shikha Mittal.
© 2024.
21 pages.
|
|
Albérico Travassos Rosário.
© 2024.
31 pages.
|
|
Carla Sofia Ribeiro Murteira, Ana Cristina Antunes.
© 2024.
23 pages.
|
|
Mario Sierra Martin, Alvaro Díaz Casquero, Marina Sánchez Pérez, Bárbara Rando Rodríguez.
© 2024.
17 pages.
|
|
Poornima Nair, Sunita Kumar.
© 2024.
18 pages.
|
|
Neli Maria Mengalli, Antonio Aparecido Carvalho.
© 2024.
16 pages.
|
|
|