THE SNARE LANGUAGE OVERVIEW
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paper_title: Taking stock of networks and organizations. A multilevel perspective

paper_content:

The central argument of network research is that actors are embedded in networks of interconnected social relationships that offer opportunities for and constraints on behavior. We review research on the antecedents and consequences of networks at the interpersonal, interunit, and interorganizational levels of analysis, evaluate recent theoretical and empirical trends, and give directions for future research, highlighting the importance of investigating cross-level network phenomena.
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paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
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Title: THE SNARE LANGUAGE OVERVIEW

Section 1: INTRODUCTION
Description 1: This section introduces the foundational concepts related to social networks, including definitions, roles, and different types of social networks.

Section 2: MODELING SOCIAL NETWORKS
Description 2: This section discusses the methods and techniques used to model social networks, such as graphical representations, mathematical analysis procedures, and statistical models.

Section 3: SNARE LANGUAGE
Description 3: This section outlines the SNARE language, detailing its purpose, main concepts, and how it supports the representation of social networks.

Section 4: DISCUSSION
Description 4: This section offers insights and reflections on the research work, the flexibility of the SNARE language, its applications, and potential future directions.