A network analysis (NA) of keyword co-occurrences for a broad collection of Data envelopment analysis (DEA) papers in the period 2008-2017 is carried out. The raw keywords have been cleaned up and standardized to consolidate and increase the consistency of the keywords. The resulting network has been characterized using network-level as well as node-level NA measures. Although the size of the network steadily increases with time, the average path length does not, showing its small world character. The disassortativity of the network indicates that the keywords used in a given paper generally include one or more common, frequently-used terms plus other less common terms that refer to the specific context of the research. The evolving nature of the keyword network is highlighted with some DEA keywords staying at the top of the ranking during the whole period and other emerging topics significantly increasing their strength during this period. The community structure of the network, which reflects the field's knowledge structure, is also presented. The identified communities generally include specific DEA methodology terms, linked with corresponding application areas as well as with some geographical references. Also, the ego-network of some sample keywords is shown, and some research gaps in DEA are identified.

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The telecommunication industry is a saturated market where a proper implementation of a retention campaign is critical to be competitive, since retaining a customer is cheaper than attracting a new one. Hence, it is crucial to detect customer behavioral patterns and define accurate approaches to predict potential churners. Multiple researchers have used binary classification methods to predict churn of customers. Some of them verify that customers' social relationships influence the decision of changing the operator.We propose a novel method to extract the dynamic relevance of each customer using social network analysis techniques with a binary classification method called similarity forests. The dynamic importance of each customer is determined by applying various centrality metrics over temporal graphs, to represent the relationships between customers and to extract behavioral patterns of churners and non-churners. These relationships are established in a temporal graph using the call detail records (CDR) of telco's customers. In this paper, we compare the performance of different centrality metrics applied over two types of temporal graphs: Time-Order Graph and Aggregated Static Graph.

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This paper presents a new approach for ranking organizational units within a benchmarking context.Instead of the conventional optimization-based techniques, the proposed approach uses Social network analysis and Multiobjective optimization concepts to extract second-order features from the input-output data, integrating the resulting multidimensional information using TOPSIS. It shows how several expert and intelligent systems techniques can be harmoniously integrated and applied to performance assessment. The proposed approach has been used for ranking the performance of 27 major US airlines, comparing the results with some existing Data Envelopment Analysis methods. It is shown that the use of a richer information set instead of the raw input-output data leads to an innovative and more effective way of discriminating between efficient units.