Social network analysis (SNA) is a core pursuit of analyzing social networks today. In addition to the usual statistical techniques of data analysis, these networks are investigated using SNA measures. It helps in understanding the dependencies between social entities in the data, characterizing their behaviors and their effect on the network as a whole and over time. Therefore, this article attempts to provide a succinct overview of SNA in diverse topological networks (static, temporal, and evolving networks) and perspective (ego‐networks). As one of the primary applicability of SNA is in networked data mining, we provide a brief overview of network mining models as well; by this, we present the readers with a concise guided tour from analysis to mining of networks. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Commercial, Legal, and Ethical Issues > Social Considerations

In online social media systems users are not only posting, consuming, and sharing content, but also creating new and destroying existing connections in the underlying social network. This behavior lead us to investigate how user structural position reacts with the evolution of the underlying social network structure. While centrality metrics have been studied in the past, much less is known about their temporal behaviors and processing, mainly when analyzing not just networks snapshots, but interval graphs. Here, we study Twitter follower/followee network and how users centralities evolve over time. Our analysis is founded on temporal graphs theory. First, we model Twitter as a temporal network and revisit the concept of shortest path considering the time dimension. We show how to compute closeness and betweenness centralities using fastest paths. Then, we propose a baseline algorithm for mining streams of temporal networks. The task is to find all pairs fastest paths inside an observation window. We find that Twitter users are fairly dynamic and from one moment to the next, they can assume (or leave) central roles in the network.

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network. Example 3 As example, let us suppose that J ohn posts 4 times about corruption (c), 3 times about sports (s), 2 times about politics ( p) and 1 time about international (i) on time 3. The temporal preferences of John on 3 are: J ohn 3 = {c J ohn 3 s, s J ohn 3 p, p J ohn 3 i, i J ohn 3 securit y, i J ohn 3 education, i J ohn 3 entertainment, i J ohn 3 economy, i J ohn 3

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