Effectively understanding and enhancing communication flows among employees within an organizational hierarchy is crucial for optimizing operational and decision-making efficiency. To fill this significant gap in research, we propose a systematic and comprehensive social network analysis approach, coupled with a newly formulated communication vector and matrix, to examine communication behaviors and dynamics in an organizational hierarchy. We use the Enron email dataset, consisting of 619,499 emails, as an illustrative example to bridge the micro-macro divide of organizational communication research. A series of centrality measures are employed to evaluate the influential ability of individual employees, revealing descending influential ability and changing behaviors according to hierarchy. We also uncover that employees tend to communicate within the same functional teams through the identification of community structure and the proposed communication matrix. Furthermore, the emergent dynamics of organizational communication during a crisis are examined through a time-segmented dataset, showcasing the progressive absence of the legal team, the responsibility of top management, and the presence of hierarchy. By considering both individual and organizational perspectives, our work provides a systematic and data-driven approach to understanding how the organizational communication network emerges dynamically from individual communication behaviors within the hierarchy, which has the potential to enhance operational and decision-making efficiency within organizations. • Develop a communication vector and matrix to describe communication patterns. • Uncover latent links between organizational hierarchy and communication network. • Reveal varying communication behavior and importance of employees based on their roles. • Demonstrate the impact of crises on organizational communication dynamics.

The usage of social media around the world is ever-increasing. Social media statistics from 2019 show that there are 3.5 billion social media users worldwide. However, the existence of community structure renders the network vulnerable to attacks and large-scale losses. How does one comprehensively consider the multiple information sources and effectively evaluate the vulnerability of the community? To answer this question, we design a gravity-based community vulnerability evaluation (GBCVE) model for multiple information considerations. Specifically, we construct the community network by the Jensen-Shannon divergence and log-sigmoid transition function to show the relationship between communities. The number of edges inside community and outside of each community, as well as the gravity index are the three important factors used in this model for evaluating the community vulnerability. These three factors correspond to the interior information of the community, small-scale interaction relationship, and large-scale interaction relationship, respectively. A fuzzy ranking algorithm is then used to describe the vulnerability relationship between different communities, and the sensitivity of different weighting parameters is then analyzed by Sobol' indices. We validate and demonstrate the applicability of our proposed community vulnerability evaluation method via three real-world complex network test examples. Our proposed model can be applied to find vulnerable components in a network to mitigate the influence of public opinions or natural disasters in real time. The community vulnerability evaluation results from our proposed model are expected to shed light on other properties of communities within social networks and have real-world applications across network science.