Data mining is being increasingly applied to social networks. Two relevant reasons are the growing availability of large volumes of relational data, boosted by the proliferation of social media web sites, and the intuition that an individual's connections can yield richer information than his/her isolate attributes. This synergistic combination can show to be germane to a variety of applications such as churn prediction, fraud detection and marketing campaigns. This paper attempts to provide a general and succinct overview of the essentials of social network analysis for those interested in taking a first look at this area and oriented to use data mining in social networks. © 2012 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Science and Technology Commercial, Legal, and Ethical Issues > Social Considerations

The study of evolution has become an important research issue, especially in the last decade, due to our ability to collect and store high detailed and time-stamped data. The need for describing and understanding the behavior of a given phenomena over time led to the emergence of new frameworks and methods focused on the temporal evolution of data and models. In this paper we address the problem of monitoring the evolution of clusters over time and propose the MEC framework. MEC traces evolution through the detection and categorization of clusters transitions, such as births, deaths and merges, and enables their visualization through bipartite graphs. It includes a taxonomy of transitions, a tracking method based in the computation of conditional probabilities, and a transition detection algorithm. We use MEC with two main goals: to determine the general evolution trends and to detect abnormal behavior or rare events. To demonstrate the applicability of our framework we present real world economic and financial case studies, using datasets extracted from Banco de Portugal Central Balance-Sheet Database and the The Data Page of New York University -Leonard N. Stern School of Business. The results allow us to draw interesting conclusions about the evolution of activity sectors and European companies.

The study of evolution has become an important research issue, especially in the last decade, due to a greater awareness of our world's volatility. As a consequence, a new paradigm has emerged to respond more effectively to a class of new problems in Data Mining. In this paper we address the problem of monitoring the evolution of clusters and propose the MClusT framework, which was developed along the lines of this new Change Mining paradigm. MClusT includes a taxonomy of transitions, a tracking method based in Graph Theory, and a transition detection algorithm. To demonstrate its feasibility and applicability we present real world case studies, using datasets extracted from Banco de Portugal's Central Balance-Sheet Database (CBSD) and the Portuguese Institute of Statistics (INE). We also test our approach in a benchmark dataset from the Time Series Data Library. The results are encouraging and demonstrate the ability of MClusT framework to provide an efficient diagnosis of clusters transitions.

In recent years we witnessed an impressive advance in the social networks field, which became a "hot" topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of temporal trajectory. Our visualization strategy is based on the definition of trajectories, both at the node-level and at the community-level, in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors, or communities of actors, by comparing the shape and position of trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.