# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 1 Introduction
However, this research used individual features of a person like a gender, hometown or number of friends instead of features gained from job activities and interactions among employees. Other articles describing the combination of SNA and standard classification methods are References [6; 7]. They both work using the Enron dataset and features based on the number of sent/received messages. In Reference [8] the usage of some network metrics as input for classification and clustering algorithms has been described. Furthermore, the results were compared to a novel measure called Human Rank (improvement of Page Rank). However, the use of classification based on social network features is not limited only to the corporate environment. For instance, following the ideas of studying the social networks of criminalists [9], the authors of Reference [10] used features of a social network of co-arrestees for predicting the possibility of future violent crimes. A similar concept was also used in Reference [11] for analyzing co-offending networks. In that work, a co-offence prediction algorithm using supervised learning has been developed. Yet, the classification in social networks based on communication or behaviour in social media, can relate to completely different areas, such as poverty detection [12], personality traits discovery [13] or occupation [14]. All that is possible because our digital traces do differ depending on our role or status. In the area of the problem being solved also many solutions concentrated mainly on classification from a graph perspective. For instance, identification of key players of social network based on entropy [15], applying graphical models [1] or factor graph models [16]. The following work focuses on the organizational structure detection based on nine-months of e-mail communication between employees of a manufacturing company located in Poland as well as the Enron dataset. The research used Decision Tree, Random Forest, Neural Network and Support Vector Machine (SVM) algorithm for classification, moreover influence of minimum employee activity was examined. The obtained results were compared with the simple graph algorithm of collective classification also proposed in this paper. The weakness of this approach is the fact that an independent and identical distribution (IID) condition is difficult to meet due to network measures which were calculated once before splitting data on training and test set. In social network analysis, full satisfaction of the IID condition is hard to achieve because if we had built independent networks for training and test data, we would get totally different network measures and the importance of each node could be biased. However, network measures could be valuable features for machine learning algorithms in sight of capturing connections between data. The results showed that the combination of classification algorithm and social network analysis can reveal organizational structures, however, small changes in the network can change the efficiency of the algorithms. Furthermore, a graph approach, such as collective classification, is able to classify well even with limited knowledge about node labels.