Detecting event-related changes in organizational networks using optimized neural network models

<div><p>Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.</p></div

<i>ROC</i> and <i>AUC</i> using different neural network models of the Enron networks.

<p><i>ROC</i> and <i>AUC</i> using different neural network models of the Enron networks.</p

Flowchart of the proposed models.

<p>Flowchart of the proposed models.</p

Examples of snapshots of the Enron networks.

<p>Examples of snapshots of the Enron networks.</p

Typical topology of a BPNN.

<p>Typical topology of a BPNN.</p

Performance comparison of the Enron networks.

<p>Performance comparison of the Enron networks.</p

Examples of snapshots of the Al-Qaeda networks.

<p>Examples of snapshots of the Al-Qaeda networks.</p

<i>ROC</i> and <i>AUC</i> using three different neural networks for the al-Qaeda networks.

<p><i>ROC</i> and <i>AUC</i> using three different neural networks for the al-Qaeda networks.</p

Classification approach for change detection.

<p>Classification approach for change detection.</p

Trends of selected measures of the Al-Qaeda networks.

<p>Trends of selected measures of the Al-Qaeda networks.</p