Nowadays, systematic collection of data has necessitated a detailed statistical analysis as a necessary tool to make a mathematical characterization of them with the purpose of gathering information about the present or the future. Our aim in this paper is to analyze a landline phone call network graph from the perspective of descriptive analysis. We explore the characteristics and structural properties of the network graph constructed using an anonymous collection of data gathered from a Call Data Records of a telecommunication operator center located in south of Albania. The R statistical computing platform is used for network graph analysis.

This study aims to explore tourist’s satisfaction on the accommodation provided during their stay in Vlore (Albania) touristic structures, and if there are possible associations between different characteristics related to this service and tourists. Lack of studies on analyzing customer satisfaction in the industry of accommodation, especially for Vlore, have prompted us to undertake this study. The study results are important for local government, the accommodation industry, and is a source of information for whom is interested to improve their accommodation services, or to invest in accommodation industry located in Vlore, Albania. “Netnography” is used to collect data for our research purpose from the reviews in TripAdvisor website. Using descriptive and inferential statistics, this study concludes that 64.9 percent of the ratings are “very good” or “excellent,” regardless of the accommodation structure chosen. Accommodation structures should have a clear defined idea of what kind of tourist they want to attract in a certain period of the year, in order to offer the quality tourists expect. Furthermore, understanding of tourist satisfaction evaluation is important in implementing successful marketing campaigns.

A collection of data is gathered from surveys held in a Spring Course of the Economic Faculty in the University "Ismail Qemali" of Vlora, Albania. The data set for each student contains the names of the other students through which he/she have a "social relationship". This relationship includes frequent communications, discussions on exercise solutions, and sitting usually close to each other in the class. We have constructed. At the end of the course, a final network based on this type of relationship. We are particularly interested on the clustering coefficient of this network and assessing it's "significance", in the sense of being somehow unusual or unexpected. Simulated random graph models, using R platform, are used to test the "significance" of the observed clustering coefficient.

This article aims to provide new results about the intraday degree sequence distribution considering phone call network graph evolution in time. More specifically, it tackles the following problem. Given a large amount of landline phone call data records, what is the best way to summarize the distinct number of calling partners per client per day? In order to answer this question, a series of undirected phone call network graphs is constructed based on data from a local telecommunication source in Albania. All network graphs of the series are simplified. Further, a longitudinal temporal study is made on this network graphs series related to the degree distributions. Power law and log-normal distribution fittings on the degree sequence are compared on each of the network graphs of the series. The maximum likelihood method is used to estimate the parameters of the distributions, and a Kolmogorov-Smirnov test associated with a p-value is used to define the plausible models. A direct distribution comparison is made through a Vuong test in the case that both distributions are plausible. Another goal was to describe the parameters' distributions' shape. A Shapiro-Wilk test is used to test the normality of the data, and measures of shape are used to define the distributions' shape. Study findings suggested that log-normal distribution models better the intraday degree sequence data of the network graphs. It is not possible to say that the distributions of log-normal parameters are normal.

Is a temporal landline phone call network graph series led by the presence of small world phenomenon? Are order and average vertex degree of the network graphs associated to small -worldness? How are related size and order of the network graphs in this temporal series? A continuously graded notion of small -world -ness is used to study the presence of small world phenomenon. Spearman's and Kendall's correlation coefficients are used to perform a non -parametric correlation analysis between small -world -ness and order/average vertex degree. Linear regression on log -transformed quantities is used to analyse the relationship between size and order. It is achieved by the study that, the presence of smallworld -ness is confirmed in each time step of the series, and there is no significant association between small -world -ness and graph order/average vertex degree. A significant positive power relationship between size and order is found.