# Reddit CrosspostNet--Studying Reddit Communities with Large-Scale Crosposst Graph Networks
## 2 State-of-the-Art of Subreddit Graph Modelling  
Reddit and other social networks are extensively studied with graph networks. The most common approach is based on capturing user-to-user relations. Here, connections (in the graph) are built using, for instance: (i) user co-commenting on post(s) [1], (ii) users commenting on each other posts [2], or (iii) a \"question-person & answer-person\" model [3] (where people who ask questions are conceptually connected with those who respond to them). Specifically, the study [1] provides a specific model for forming connections between users, based on their co-commenting. They account not only for the co-commenting, but also for the number of commented posts and the similarity of comments. Here, the weight of the connection is a product of the number of comments and the similarity measure. The latter one is called \"mutual interest\" and it is defined as \((P_{\text{si}}\cap P_{\text{gi}})/(P_{\text{si}}\cup P_{\text{gi}})\), where \(P_{\text{si}}\) denotes the number of posts the user \(U_{\text{si}}\) commented on and \(P_{\text{gi}}\) denotes the number of posts the user \(U_{\text{gi}}\) commented on. The authors use a dataset of 54,504,410 comments with 2,611,445 authors. They take three samples and a frequent set of users and build three networks. All of them contain about 9700 nodes and about 670,000 edges (after data cleaning). Next, the study analyzes the basic social network properties of said graphs. Moreover, community detection is performed and its results are analyzed. The key results of this work will be further discussed in the context of the network built in this contribution. In the second study [2], the authors gather data from 24,298 subreddits, posts (a.k.a. submissions), and comments. Then the inter-subreddit relations are modeled, based on links in the subreddit description, resulting in a graph with 27,091 nodes and 9050 edges. The completed analysis includes measuring the distribution of node degree, triangles, largest connected component, and correlations between network measures and subreddit subscribers count. Furthermore, a tree-like structure of comments of posts is analyzed. Here, comments form a tree graph--a directed acyclic graph and the conducted research checks how many nodes (subreddits) have what number of comments and what is the tree depth. Furthermore, three new graphs are built. They are based on users, with 31,731 nodes, 1,804,974 edges (the \"loose graph\"), 176,385 nodes and 435,413 edges (the \"tight graph\"), and 13,802 nodes and 13,251 edges (the \"strict graph\"). The edges in the loose graph exist if user A commented on user B (undirected). The edges in the tight graph exist if user A commented on user B and user B commented on user A. The edges in the strict graph exist if user A commented on User B four times and vice versa. For these three graphs, the study measures (again) the basic network statistics, such as the node degree and the connected components. The results of the study introduce the analyzed dataset and summarize the findings, though without additional interpretation. The last study in this group (ref. [3]), analyzes the so-called \"question-answer\" model. Here, only the \"Ask\" subreddits, AskScienceDiscussion, AskMen, AskScience, AskWomen, CompSci, DesMoines, IAmA, MachineLearning, Movies, MyLittlePony, PersonalFinance,TalesFromTechSupport, and WashingtonDC, subreddits are studied. This is because the posts in these subreddits are mostly questions, while the comments provide answers. Using the top 100 posts from one month and an average of 200 comments per post, a network is instantiated. The nodes are users and the directed edges are defined if an \"answering\" user commented on posts of a \"questioning\" user. This way, the question users are nodes with very high in-degree and low out-degree, while the answering users are the opposite. Although this analysis focuses on a very particular relation, it uses very universal network analysis methods.