# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 4 Discussion  
The comparison of the results for standard classification algorithms and collective classification show that the first of them cope better with balanced data such as the Enron datasets. The conducted experiments also present that Decision Tree and Random Forest, as well as Neural Network and SVM algorithm, have been able to obtain similar results. However, in an organizational environment, the possibility of the result interpretation could be highly appreciated, therefore the first two algorithms are a better solution if we would like to deeply understand the communication behavior on different organizational hierarchy levels. Furthermore, the presented collective classification algorithm obtained better results in the case of an unbalanced dataset. These results indicate that the graph algorithm is able to reduce impact of majority classes and predict well contrary to the standard classification algorithms. In addition, future research should examine if the above impact of unequal classes is also associated with some network hidden characteristics. Furthermore, it was presented that the minimum length of the period in which an employee was active influences the result, however the ideal value of the minimum activity depends on an analyzed dataset, as well as other parameters. A network created from email communication may vary from organization to organization, especially regarding to the size of the company and different management models, for example: a big international company with many employees and a complicated hierarchy could create a social network with totally different properties than a small startup with a bunch of employees and a simple structure. Moreover, in some companies email communication could be one of many ways of passing messages, so a dataset of emails do not have to contain full information about the connection between employees in the company. These differences could cause some patterns of behavior assigned to a specific level of hierarchy, which does not have to appear in a constructed social network. Figure 8: The result of the collective classification of three groups for the Enron company dataset. Future work should focus on the study of communication coming from different types of companies, moreover, further research should discover which organizational structures can provide the best results of classification task. Therefore, better results could be an implication of some hidden graph properties corresponding to the way which an organization is managed, so future studies also should focus on the examination of a network structure and revealing its characteristics. The biggest problem may be obtaining data for research due to the fact that internal communication of a company is confidential and has to be anonymized before being shared. Another interesting approach is the attempt to use graph embeddings instead of conventional features provided to supervised learning algorithms. This way the properties of nodes will be encoded in a form of vectors making them more suitable as direct input to algorithms. Regarding the collective classification, instead of analysing particular features as the input for utility score, latent Dirichlet allocation could be used to create a utility score combining features. Lastly, the reader would notice that the social network used in this study has been an aggregated one. This was mainly due to the fact that organizational structure of manufacturing company did not undergo any changes in the period covered by the dataset and in the case of Enron, the structure was inferred from e-mails and no other information was known. However, for applying proposed approach in organizations, it would be advised to verify the capabilities of temporal approach: both in the area of measures as well as networks.