Abstract
Nowadays, treating the data as a continuous real-time flux is an exigence explained by the need for immediate response to events in daily life. We study the data like an ongoing data stream and represent it by streaming egocentric networks (Ego-Networks) of the particular nodes under study. We use a non-standard node forgetting factor in the representation of the network data stream, as previously introduced in the related literature. This way the representation is sensible to recent events in users' networks and less sensible for the past node events. We study this method with large scale Ego-Networks taken from telecommunications social networks with power law distribution. We aim to compare and analysis some reference Ego-Networks metrics, and their variation with or without forgetting factor.

Abstract
Discovering communities in a network is a fundamental and important problem to complex networks. Find the most influential actors among its peers is a major task. If on one side, studies on community detection ignore the influence of actors and communities, on the other hand, ignoring the hierarchy and community structure of the network neglect the actor or community influence. We bridge this gap by combining a dynamic community detection method with a dynamic centrality measure. The proposed enhanced dynamic hierarchical community detection method computes centrality for nodes and aggregated communities and selects each community representative leader using the ranked centrality of every node belonging to the community. This method is then able to unveil, track, and measure the importance of main actors, network intra and inter-community structural hierarchies based on a centrality measure. The empirical analysis performed, using two temporal networks shown that the method is able to find and tracking community leaders in evolving networks. © 2020, Springer Nature Switzerland AG.

Abstract
Panel data is a regression analysis type that uses time data and spatial data. Thus, the behavior of groups, for example, enterprises or communities, is analyzed through a time scale. Panel data allows exploring variables that cannot be observed or measured or variables that evolve over time but not across groups or communities. In this chapter, two different techniques used in panel data analysis is explored: fixed effects (FE) and random effects (RE). First, theoretical concepts of panel data are presented. Additionally, a case study example of the use of this type of regression is provided. Panel data analysis is performed with R language, and a step-by-step approach is presented.

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