http://jms.sciedupress.com Journal of Management and Strategy Vol. 11, No. 3; 2020 Published by Sciedu Press 55 ISSN 1923-3965 E-ISSN 1923-3973 Exploring Interdisciplinary Relationships Among King Abdulaziz University Departments via ResearchGate: Network Analysis and Visualizations Hanan M. Baaqeel 1 , Sara F. Aloufi 1 & Tariq Elyas 2 1 Department of Statistics, KAU—King Abdulaziz University, Jeddah, Saudi Arabia 2 Department of European Languages and Literature, KAU—King Abdulaziz University, Jeddah, Saudi Arabia Correspondence: Tariq Elyas, Department of European Languages and Literature, KAU—King Abdulaziz University, Jeddah, Saudi Arabia. E-mail: telyas@kau.edu.sa Received: August 20, 2020 Accepted: September 21, 2020 Online Published: September 26, 2020 doi:10.5430/jms.v11n3p55 URL: https://doi.org/10.5430/jms.v11n3p55 Abstract Because all disciplines are connected, interdisciplinary studies are one of the most significant discussions in the education sector. It involves the merging of two or more academic disciplines into one activity. The aim of this research paper is to explore the relationship of interdisciplinary research and network among all departments at King Abdulaziz University (KAU) in ResearchGate (RG) by using the statistical network analysis of undirected social networks. In our academic network, the departments of the university represent the vertices and their academic relationships. We will detect the communities between the departments in RG network by using statistical analysis of the network for each community. Finally, we will compare the academic social network at KAU to some random graph models, and investigate some random graph characteristics, such as power-law, small-world, and scale-free models. In our research, we found that the Department of Chemistry has the highest degree for the academic social network at KAU in RG, and the highest eigenvector centrality as well. In terms of vertex centrality, the Department of Electrical and Computer Engineering has the highest value in closeness and betweenness centrality. Also, we found that the most two connected departments are the Department of Computer Science and Department of Physics through the edge weight equals 248. By using community detection, we found there are seven communities. We conclude that the degree distribution of the academic social network of KAU in RG is different from the degree distribution of random graph models, but it is slightly close to small world model. This study , in turn, can participate to achieve one of the goals of Vision 2030 by shedding some light into how to improve research networks in the education sector and research among Saudi universities. Keywords: community deduction, descriptive network analysis, random graph models, interdisciplinary research, KAU, ResearchGate, research network 1. Introduction A social network consists of a set of vertices connected by edges where each edge connects the two vertices, where the edges can have a direction. Analysis of social networks is similar to a traditional statistical analysis in its steps but differs in its tools. First, the social networks are first studied through the network representation. The descriptive analysis of the network is the second step. Then, the statistical analysis of the social network is done by modeling and examining of the model. In our research paper, we are interested in the descriptive analysis of undirected academic social networks basic description, characteristics vertex and edges, sub- groups, and graph partitioning. The social network used in this research is the academic social network among all the departments at KAU within the RG platform. 2. Literature Review Interdisciplinary research has become more common in scientific research and defined as the integration of disciplines within a research field. A study by (Hu & Zhang, 2017) conducted on the social network by taking the data from Web of Science used statistical analysis of the network. The results were irregular disciplinary distribution and that the most effective disciplines are Computer Science, Engineering, Business, and Economics. However, Mislove, Marcon, Gummadi, Druschel, & Bhattacharjee (2007) used the other important online social networks: