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| Social network analysis (SNA) has been a research focus in multiple disciplines for decades, including sociology, healthcare, business management, etc. Traditional SNA researches concern more human and social science aspectsV trying to undermine the real relationship of people and the impacts of these relationships. While online social networks have become popular in recent years, social media analysis, especially from the viewpoint of computer scientists, is usually limited to the aspects of people's behavior on specific websites and thus are considered not necessarily related to the day-today people's behavior and relationships. We conduct research to bridge the gap between social scientists and computer scientists by exploring the multifacet existing social networks in organizations that provide better insights on how people interact with each other in their professional life. We describe a comprehensive study on the challenges and solutions of mining and analyzing existing social networks in enterprise. Several aspects are considered, including system issues; privacy laws; the economic value of social networks; people's behavior modeling including channel, culture, and social inference; social network visualization in large-scale organization; and graph query and mining. The study is based on an SNA tool (SmallBlue) that was designed to overcome practical challenges and is based on the data collected in a global organization of more than 400 000 employees in more than 100 countries.

In large scale online multi-user communities, the phenomenon of 'participation inequality,' has been described as generally following a more or less 90-9-1 rule [9]. In this paper, we examine crowdsourcing participation levels inside the enterprise (within a company's firewall) and show that it is possible to achieve a more equitable distribution of 33-66-1. Accordingly, we propose a SCOUT ((S)uper Contributor, (C)ontributor, and (OUT)lier)) model for describing user participation based on quantifiable effort-level metrics. In support of this framework, we present an analysis that measures the quantity of contributions correlated with responses to motivation and incentives. In conclusion, SCOUT provides the task-based categories to characterize participation inequality that is evident in online communities, and crucially, also demonstrates the inequality curve (and associated characteristics) in the enterprise domain.

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