## 2 Related Work
The unavailability of real-world social network data has had detrimental effects on research related to information propagation in real-world networks. It has also stymied research related to user behaviors that can have negative effects on the society [18]. However, common social network analysis tasks such as community detection, whether it be through explicit means or implicit ones, have not been affected. This is primarily due to availability of network datasets that are either based on the real world or mimic real-world social interactions implicitly or in restricted contexts. For example, the networks of the Karate Club and DBLP are real-world network datasets which have been used for testing community detection techniques [19; 20]. These datasets, though not reflective of general social networks today, allow for the methods and algorithms for detecting community structures to be tested. Understanding community structures, whether explicit or implicit, plays an important role in understanding the dynamics of social networks, and hence there have been a lot of studies on understanding how they form and how to find them in cases where explicit structures are missing. Community detection has been used for detecting hidden communities, cross communities, community and group formation dynamics, as well as information flows through social networks. In [21], the authors proposed a method for detecting the smallest and most granular multi-profiled cross-community, to which an active user belongs. They tested their proposed approach, which is based on the idea of \(k\)-cliques in the DBLP dataset. The DBLP dataset includes a comprehensive list of research articles in computer science converted into co-authorship networks. However, since the dynamics of co-authorship are different from the dynamics of a social network like Reddit, this study might have limited applicability to large-scale real-world social networks. In [22], the authors discussed a socio-semantic network approach to identify communities based on altmetric data. By clustering social media users according to their interests in research topics revealed by shared research publications, the study aimed to eliminate biases from social proximity, thus revealing disconnected users with similar academic interests. The authors tested the proposed method for users tweeting publications from the fields of Information Science and Library Science, and Microbiology. In [23], the authors studied the problem of identifying hidden communities, which are defined as weaker community structures that are embedded within the stronger communities found on a social network. Real-world networks contain sparse community structures, such as secret organizations or temporary groups, which are considerably weaker than the dense community structures like families, colleagues, or close friends. The work sought to address the problem of detecting the weaker communities from these networks. They tested the proposed methods on datasets from Facebook, Amazon, YouTube [4], and DBLP. We must point out that the largest network that was used had around 50,000 users. This is small compared with the sizes of most modern social networks and hence it was hard to evaluate the efficacy and feasibility of the proposed methods on large, real-world social networks that have hundreds and thousands of users. Moreover, the datasets are also dated and may not reflect current social dynamics. Another line of inquiry has looked into community structures across multiple social networks. For example, in [24], the authors presented a community detection method based on nonnegative matrix trifactorization for multiple heterogeneous social networks. The paper emphasized the importance of detecting overlapping users who bridge different social networks by being simultaneously affiliated with them. The approach created adjacency matrices based on structural and content similarities and utilized alignment matrices to enhance community fusion across networks.