Shannon entropy applied in networks based on titles of scientific papers

Recently, semantic networks based on titles of scientific papers have been explored to study the formal system of scientific communication: the journal. The network theory along with the Shannon entropy of information can give interesting insights on how to differentiate journals.
We present an application of Shannon entropy in semantic networks based on titles of scientific papers. These networks are formed considering the words of a title as vertices of a click. The titles come from $15$ scientific journals high-impact. For a given network is also built a time-varying graph (TVG). We consider a window bimonthly (week by week) and advancement in time shows the evolution of networks of words from the titles of papers published. The analysis is carried out using indicators timeless. Entropy is calculated based on the uncertainty of edges and vertices of each network. The probabilities are associated with overlapping edges and vertices in the formation of the titles network, taking into account the relative frequency of these indicators before clicks are joined. Thus, it becomes possible to compare different journals, as well as a same journal at different times (through the TVG). The results suggest that these journals can be grouped, based on their entropy values and indices of some networks. This study may contribute to the study of communication and scientific collaboration and dissemination of human knowledge.