## 5 Experimental Results and Their Analysis
### Exploration of Particular Subreddit Pairs  
Let us now briefly share the most interesting results obtained in the cases where the algorithm reached the highest recall and AUC. These results are summarized in Table 3. There, when applicable, metrics values are given in parentheses, with the following demarcation: \(r\), \(=\)_recall@10_ and \(a\). \(=\)_AUC_. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subreddit Pair** & **Entities** & **Recall [\%]** & **AUC [\%]** & \
\begin{tabular}{c} **Metadata/Network** \\ **Characteristic** \\ \end{tabular} \\ \hline r/SteamDeck, & & & & \\ r/totalwar & & & & \\ \hline r/SteamDeck, & & & & \\ r/totalwar & & & & \\ \hline r/manga, r/Meika & & & & & \\ \hline r/manga, r/Meika & & & & & \\ \hline r/PomStars, & & & & & \\ r/KristyBlack & & & & & \\ \hline r/MovieDetails, & & & & & \\ r/UnexpectedMulaney & & & & & \\ \hline r/hqcelebvideos, & & & & & \\ r/EmmaWatson & & & & & \\ \hline r/nvidia, r/AyyMD & & & & & \\ \hline \multirow{2}{*}{r/nvidia, r/AyyMD} &
\begin{tabular}{c} AMD, EVGA, NVIDIA \\ GeForce, RTX \\ \end{tabular} & & & & \\ \hline \multirow{4}{*}{r/carporn, r/NASCAR} & Circuit Zolder, Death & & & & \\ r/carporn, r/NASCAR & & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline r/German, r/germany & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & \\ \hline r/Borderlands2, & & & & & \\ r/Borderlands3 & & & & & \\ \hline r/graphic\_design, & & & & & \\ r/technology & & & & & \\ \hline r/onions, r/ethereum & & & & & \\ \hline r/frugalmalefashion, & & & & & \\ r/eagles & & & & & \\ \hline r/Piracy, & & & & & \\ r/vpnnetwork & & & & & \\ \hline r/vpnnetwork, & & & & & \\ r/fantsaybball & & & & & \\ \hline r/[PERSON], r/IAMA & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline r/Kanye, r/IAMA & & & & & \\ \cline{1-1}  & & & & & \\ \hline r/Kanye, r/IAMA & & & & & \\ \cline{1-1}  & & & & & \\ \hline r/Kanye, r/IAMA & & & & & \\ \cline{1-1}  & & & & & \\ \hline r/[PERSON], r/IAMA & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline r/AITA, & & & & & \\ r/justdependathings & & & & & \\ \hline r/AITA, & & & & & \\ r/weddingshaming & & & & & \\ \hline r/AITA, r/houseplants & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected similarities between subreddits with the highest recall and AUC. Let us now summarize the most interesting findings. First, many similarities occur when one of the subreddits has a strictly narrower or wider topic, and it is this topic (named entity) that is the common one for the two subreddits. For example, r/SteamDeck (gaming console) and r/totalwar (video game) share entities Steam Deck and Total War (_score_: r. 100%, a. 76%, _number of comments_: r. 100%, a. 80%). Between r/manga and r/Meika (a Manga character) it is Meika that is the most common (degree: r. 81%, a. 78%, average neighbor degree,: r.82%, a. 100%). Between r/PornStars and r/[PERSON] (a porn actress) the entity is [PERSON] (_total awards received_: r. 89%, a. 80%); for r/MovieDetails and r/UnexpectedMulaney (subreddit about references to [PERSON]), [PERSON] is the most common topic (degree, r63%, a. 98%); r/hqcelebvideos (subreddit about celebrities) and r/EmmaWatson share [PERSON] as the main similarity (degree centrality r. 86% a. 82%). Second, there exist groups of subreddits that intuitively could have been expected to be similar or have common grounds. For example, r/nvidia and r/AyyMD are both about digital computing companies and the subreddits share many models of GPUs, such as AMD, EVGA, NVIDIA GeForce, RTX (_score, number of comments_ and has image all have recall over 83% and AUC over 73%). Another example is r/carporn (related to beautiful cars) and r/NASCAR, where the common grounds are names of races and cars (e.g., Circuit Zolder, Death Valley, NASCAR Camaro, Suzuki, XB Falcon) (degree centrality r. 67%, a. 67%).