Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social net- work. 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 Net- work 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.
The Brain is a Social Network
Claudio Tomazzoli
;Silvia Francesca Storti
;Ilaria Boscolo Galazzo
;Matteo Cristani
;Gloria Menegaz
2017-01-01
Abstract
Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social net- work. 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 Net- work 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.
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