Compensatory fuzzy logic for intelligent social network analysis
Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | Spanish |
| Published: |
Universidad de Ciencias Informáticas
2014-10-01
|
| Series: | Revista Cubana de Ciencias Informáticas |
| Subjects: | |
| Online Access: | http://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=807&path[]=301 |
| Summary: | Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with our model are more consistent with the way human make decisions. Additionally a case study to illustrate the applicability of the proposal on a coauthorship network is developed. Our main outcome is a new model for social network analysis based on compensatory fuzzy logic that gives more robust results and allows compensation. Moreover this approach makes emphasis in using language for social network analysis. |
|---|---|
| ISSN: | 1994-1536 2227-1899 |