Mental model representation using fuzzy graphs have recently grown in popularity for decision support and knowledge representation. Finding the most important node in the model has multiple applications. This paper presents a new model for static analysis in fuzzy graphs applied to the learning process. It makes use WA operators for the aggregation of the different centrality measures. This composite measure make possible to order the nodes and select the most important in a more integral way. WA operator brings flexibility to the model. A case study to show the applicability of the proposal is presented.
Fuzzy cognitive maps have received increasing attention for the representation of the causal knowledge especially useful in knowledge management. This paper proposes a model CRM critical success factor modelling and analysis based on fuzzy cognitive maps and using the paradigm of Computing with Words, in order to provide causal models easy to understand. To this end, the use of linguistic representation model based on linguistic 2-tuple in the competitive fuzzy cognitive maps is proposed, which allowing to perform the Computing with Words Processes without losing information. The main advantage of the model proposed for is that it allows increasing the interpretability of the causal models, being this fact knowledge management. Last, the paper presents a case study of the model proposed, as well as recommendations for future works.
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