## 2 Related Work
The experimental results demonstrated superior performance on datasets from Twitter, Instagram, and Tumblr. However, the datasets were obtained between January 2017 and 2018 and contained only a few thousand users as opposed to the hundreds to thousands of users in modern-day social networks. Moreover, we must also point out that the same user may use different social networks with different user handles. This makes it hard to associate an user in one social network to another user handle on a different social network. In fact, this problem of user association is of independent interest and has been looked at in the context of social networks [21; 25]. This problem has not been taken into consideration in the work by [PERSON] et al. and this limits the applicability of the results to real-world settings. Work on large-scale real-world social networks has sometimes used data from countries other than the US. For example, in [26], the authors studied the problem of detecting implicit communities in large-scale real-world social networks. They used data obtained from the Chinese WeChat platform for testing the efficacy of the proposed methods. Similarly in [27], the authors used data from WeChat to study structural features of WeChat communities (groups). They presented results on how geographical factors influence online interactions within WeChat. In [28], the authors analyzed data obtained from the Russian social networking platform VK to study the reposting activity of viral advertisements. Similarly, the authors studied the problem of estimating the behavior frequency of users using a Bayesian belief network in [29]. They tested the proposed method on data obtained from VK. In [30], the authors studied politicized virtual communities in VK. While these datasets are from the real world, in order to use these datasets, one needs to be familiar with the languages of the corresponding countries, Chinese and Russian, in these two cases. Furthermore, due to the political and social structures of these countries, the behavior of the users might be different from those in the western world. Thus, results from these studies, though reflective of real-world social behavior, may not be directly portable to social networks in the US or Europe. In fact, studying the differences in social behavior across countries, cultures, and continents is an active area of research [31]. Though the problem of detecting communities on social network platforms has been widely studied, the associated problem of how different communities on real-world social networks interact has not been studied quite so much. In a recent work [32], the authors studied how fringe communities thrive and grow on large social network platforms like Reddit. Precisely, this study investigates how interactions between members of fringe communities and users from mainstream communities on Reddit can lead to the growth of fringe communities. In [33], the authors studied the concept of cross-community mining (CCM). CCM aims to connect different forms of communities and study the interaction among them. It covers various aspects of CCM, including property association, data aggregation, knowledge transfer, and cross-community information dissemination. In this work, we study the interactions between explicit communities on Reddit, called \"subreddits\", with the goal of identifying a group of subreddits that are \"highly interconnected\". We achieved this through the study of interactions between the users of subreddits. Note that we did not consider fringe or mainstream subreddits separately. We considered all subreddits and identified the group of highly interconnected subreddits through analysis of user interactions across all subreddits. In effect, we identified implicit groups created by the interaction of users across subreddits. We also identified the implicit leaders and followers in the subreddits with the goal of associating the nature of the subreddits with the leaders.