Formation of a hierarchy within an organization is a natural way of optimizing the duties, responsibilities and flow of information. Only for the smallest organizations the lack of the hierarchy is possible, yet, if they grow, its appearance is inevitable. Most often, its existence results in a different nature of the tasks and duties of its members located at different organizational levels. On the other hand, employees often send dozens of emails each day, and by doing so, and also by being engaged in other activities, they naturally form an informal social network where nodes are individuals and edges are the actions linking them. At first, such a social network may seem distinct from the organizational one. However, the analysis of this network may lead to reproducing the organizational hierarchy of companies. This is due to the fact that that people holding a similar position in the hierarchy can possibly share also a similar way of behaving and communicating attributed to their role.The key concept of this work is to evaluate how well social network measures when combined with other features gained from the feature engineering align with the classification of the members of organizational social network. As a technique for answering the research question, machine learning apparatus was employed. Here, for the classification task, Decision Tree and Random Forest algorithms where used, as well as a simple collective classification algorithm, which is also proposed in this paper. The used approach allowed to compare how traditional methods of machine learning classification, while supported by social network analysis, performed in comparison to a typical graph algorithm.

This work develops the concept of the temporal network epistemology model enabling the simulation of the learning process in dynamic networks. The results of the research, conducted on the temporal social network generated using the CogSNet model and on the static topologies as a reference, indicate a significant influence of the network temporal dynamics on the outcome and flow of the learning process. It has been shown that not only the dynamics of reaching consensus is different compared to baseline models but also that previously unobserved phenomena appear, such as uninformed agents or different consensus states for disconnected components. It has also been observed that sometimes only the change of the network structure can contribute to reaching consensus. The introduced approach and the experimental results can be used to better understand the way how human communities collectively solve both complex problems at the scientific level and to inquire into the correctness of less complex but common and equally important beliefs’ spreading across entire societies.

Mobile phones contain a wealth of private information, so we try to keep them secure. We provide large-scale evidence that the psychological profiles of individuals and their relations with their peers can be predicted from seemingly anonymous communication traces-calling and texting logs that service providers routinely collect. Based on two extensive longitudinal studies containing more than 900 college students, we use point process modeling to describe communication patterns. We automatically predict the peer relationship type and temporal dynamics, and assess user personality based on the modeling. For some personality traits, the results are comparable to the gold-standard performances obtained from survey self-report data. Findings illustrate how information usually residing outside the control of individuals can be used to reconstruct sensitive information.