Name
Abstract | ||
|---|---|---|
Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: degree centrality, closeness centrality and betweeness centrality. We developed a model, using Semantic Social Network Analysis, that overcomes the drawbacks of general indices and we found that this model can be applied, after appropriate adaptations, to a very different domain such as brain connectivity. |
Year | Venue | DocType |
|---|---|---|
2017 | CEUR Workshop Proceedings-Series | Conference |
Volume | ISSN | Citations |
1959 | 1613-0073 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Tomazzoli | 1 | 25 | 11.36 |
| Silvia Francesca Storti | 2 | 9 | 4.35 |
| Ilaria Boscolo Galazzo | 3 | 5 | 5.13 |
| Matteo Cristani | 4 | 259 | 34.75 |
| Gloria Menegaz | 5 | 75 | 10.73 |