Community Detection and Modularity Analysis in Stack Overflow Network Graphs

Stack overflow is the largest online platform for learning and exchanging question answers for the computer programmers. On Stack Overflow, participants share topics that help programming learner, professionals, and experts. Using specific words or tags to describe a question makes it easier to explore the site. Tags are helpful in finding answers faster and connect with others who know about that topic. In this study we have chosen the stack overflow data set where in the first step exploratory data analysis is performed. Community detection algorithm is applied on 115 number of nodes and 490 number of links of the stack overflow dataset. In this paper, we use different community detection techniques and centrality metrics to analyses community detection in computer programming classes offered on Stack Overflow, a popular platform. To be more precise, we examine the community structure in the Stack Overflow courses using the Girvan-Newman, Louvain, Walk-trap, and Greedy modular community algorithms.Additionally, we assess the modularity of the identified communities by utilizing the Girvan-Newman algorithm, which measures the degree of network segmentation into modules. Furthermore, we provide valuable insights into the organization and clustering of users in online learning platforms, facilitating the development of targeted interventions and community management strategies to enhance the learning experience.