# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 1 Introduction  
People around the world send hundreds of emails to exchange information within organizations. As an implicit result of that, each of these interactions forms a link in a social network. This network can be a valuable source of knowledge about human behaviors and what is more, conducting the analysis can reveal groups of employees with similar communication patterns. These groups usually coincide with different levels of the organization's hierarchy and additionally, employees who work in the same position generally have a comparable scope of duties. It is common for organizations to observe some hierarchy because, formally, an organized structure helps with better management of employees and gaining an advantage within the market. Therefore, the analysis of the network created from a set of emails could retrieve valuable data about inner corporation processes and recreate an organizational structure. An interesting and promising idea seems to be the combination of network measures and additional features extracted from messages for classification tasks. Social network analysis (SNA) hasthe potential to boost machine learning algorithms in a field of organization structure detection, thus capturing relations between data seems to be very important for this kind of dataset. The reverse engineering of the corporate structure of an organization can be perceived two-way. On the one hand, if successful, it could reveal company structure by having only meta-data and this imposes a risk in the case when the structure is intentionally kept secret, for example, for keeping the competitive edge or protecting the employees from takeovers by other companies. On the other hand, this could lead to reconstructing the structure of malicious organizations when only partial information about them is available. In the literature, there are several works describing the detection of organizational structures. However, most of them use the Enron dataset [1] or focus rather on a network approach and omit standard supervised classification algorithms. It should be noted that each organization is managed in a slightly different way which means that communication patterns could differ within each of them. These differences imply that some solutions may give better or worse results depending on the network's specificity; it is important that studies on an organization hierarchy should not be limited to only one dataset. The authors of Reference [2; 3] introduced a concept of matching a formal organizational structure and a social network created from email communications. Experiments were carried out on messages coming from a manufacturing company located in Poland as well as the well-known Enron dataset. The research results showed that in both cases, some network metrics were able to reveal organizational hierarchy better than others. This work also touched on the problem that a formal structure sometimes may significantly differ and will not converge with the daily reality. The idea of combining network metrics and other features extracted from email dataset to reveal corporate hierarchy is introduced in Reference [4]. The authors presented their own metric named \"social score\" which defines the importance of each employee in the network. This metric is defined as a weighted average of all features and is used in a grouping algorithm. The grouping method is a simple straight scale level division algorithm which assigns employees to defined intervals by the social score. The study on the usage of network measures as input features for classification algorithms was presented in Reference [5]. The basic concept of this work focused on retrieving company hierarchy based on the network created from social media accounts of the employees. The authors presented that centrality measures and clustering coefficients in combination with other features extracted from social media can detect leaders in a corporate structure.