# Reddit CrosspostNet--Studying Reddit Communities with Large-Scale Crosposst Graph Networks
## 4 Characterizing CrosspostNet
### Community Analysis  
Here, first, we consider the average clustering coefficient in the undirected graph, which measures the average of local clustering coefficients (the fraction of triangles that exist divided by all possible triangles in the node's neighborhood). The obtained value is 0.153. It is similar to the one reported in the previous study (ref. [10]), where it oscillated in time between 0.1 and 0.2. This shows that approaching the Reddit network from the point of view of cross-linking and crossposting produces communities with a similar number of possible triangles in the graph. Going further, the degree assortativity can be checked. This measure explains the correlation between node degree and its neighbor node degree. The degree assortativity (Pearson correlation coefficient) ranges between \(-1\) (disassortative network, i.e., nodes connect to nodes with different degrees) to 1 (assortative network, i.e., nodes connect to nodes with similar degrees). In the previous study [10], the reported value was about 0.0. Here, the calculated value was \(-0.04\). This means that the link-based graph, studied in 2020 and the crosspost-driven subreddit graph, from 2022, are both neither assortative nor disassortative. In other words, there is _no relationship_ between the degree of a given node and that of its neighbors. Next, we consider the community analysis. Studies on user-based social networks showed interesting results when applying algorithms for community discovery, such as the Louvain method [17; 18]. The first work [17] analyzes inter-subreddit conflicts of 2016. It creates a co-conflict network, where nodes are subreddits, and applies different algorithms and methods to analyze it. Among them, the community detection is used to determine, which groups of subreddits (rather than pairs) are co-targeted. What is important is that the authors mention three community detection algorithms (FastGreedy [19], InfoMap [20] and Louvain methods [21]), briefly compare them, and, finally, decide that the Louvain algorithm is the best for the Reddit graph network. Louvain's method is based on modularity maximization (a measure of cohesiveness of the network). The authors chose the Louvain algorithm over the remaining algorithms because it \"follows a hierarchical approach by first finding small, cohesive communities and then iteratively collapsing them in a hierarchical fashion (...) produces reasonably sized communities and the results of the community detection algorithm were very stable\". Learning from the findings of that study, the Louvain method is also applied here, to find the communities of subreddits connected with crossposts. The Louvain algorithm found 796 different communities. Among them, there are 575 (72%) communities of size 2 and 117 of size 3 (15%). This totals over 87% of all communities consisting of 2-3 subreddits. On the other hand, 4 large communities have been detected. The largest community contains, 1334 subreddits and there is no particular theme among them (as far as we could establish on the basis of manual inspection). The subreddits are about multiple topics, ranging from politics, to memes, to pictures of animals. This means that the majority of crossposts \"connect\" vaguely related subreddits, i.e., there is no particular largest thematic community of subreddits that has strong connections based on crossposts. However, the second-largest community (876 subreddits) has a very visible theme. All subreddits in the second-largest community contain mostly pornography. The third-largest community (571 subreddits) contains mostly subreddits that concern politics, or are related to individual countries. Finally, the last one (539 subreddits) is also about pornography, but with a definite focus on the drawn/animated works (with hentai drawings represented in the majority of them).