## 5 Experimental Results and Their Analysis
Moreover, the best AUC results achieved for the network characteristics are as follows: degree centrality (60%), degree (51%) and clustering (50%). Therefore, the best recall and AUC results are in the middle of the range found in other, similar studies. However, we note also that none of the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Recall** & **Recall** & **Recall** & **AUC** & **AUC** & **AUC** \\ \hline  & **All** & **Isolates** & **Periphery** & **All** & **Isolates** & **Periphery** \\ \hline average\_neighbor\_degree & 0.20 & 0.31 & 0.67 & 0.57 & 0.52 & 0.56 \\ \hline clustering & 0.13 & 0.25 & 0.62 & 0.50 & 0.50 & 0.51 \\ \hline degree & 0.17 & 0.30 & 0.67 & 0.52 & 0.52 & 0.56 \\ \hline is\_original\_content & 0.18 & 0.26 & 0.60 & 0.46 & 0.49 & 0.48 \\ \hline has\_image & 0.28 & 0.31 & 0.62 & 0.20 & 0.19 & 0.32 \\ \hline num\_comments & 0.35 & 0.43 & 0.73 & 0.24 & 0.28 & 0.46 \\ \hline score & 0.38 & 0.47 & 0.76 & 0.26 & 0.31 & 0.50 \\ \hline total\_awards\_received & 0.35 & 0.41 & 0.66 & 0.24 & 0.29 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recall@10 and AUC results for different metadata and network characteristics. The “Isolates” column shows results for graphs with many isolate nodes (over 50% of the network nodes are isolates, i.e., have no neighbors). Periphery refers to results where the periphery was large (over 50%). cited works dealt with a dataset with tens of thousands of pairs of compared subreddits. Separately, the question as to what should be reasonably expected when measuring recall for the node-based metadata and the AUC for the network-based metadata requires further investigation. However, this is out of scope of this contribution. Since there was no single best similarity metric, additional attempts at combining the rankings to achieve better results were completed. Taking the top results from two separate rankings (for example, top five _score_-based entities and top five degree-based entities) did not yield better results. In most cases, the obtained results were simply an average of two rankings, which makes this worse than accepting the better ranking of the two. In the next steps, the obtained results were further explored. First, a working hypothesis was posed that topically close subreddits, like subreddits about gaming (e.g., like r/gaming, r/esports, r/Games, r/pcgaming), or politics and news (e.g., r/politics, r/news, r/worldnews), have better recall and AUC scores. Although there are solitary cases described in Section Exploration of Particular Subreddit Pairs, no major noticeable correlation between the quality of results and subreddit topical areas was observed. Second, it was postulated that subreddits with similar named entity network structures (measured with network characteristics) may deliver better quality of results. This assumption was correct. Networks with larger peripheries (measured as the set of nodes with eccentricity [96] equal to the diameter) achieved recall values that were better by approximately 30-60 percentage points. Similarly, networks with a large number of isolates (nodes with a degree equal to zero) achieved results approximately eight percentage points higher than the mean. Upon further reflection, this phenomenon is easy to explain. In most cases, networks with a large periphery or with many isolates happen to also have a low number of high-degree nodes. These high-degree nodes are the focal points of the network (i.e., of discussion). Hence, they achieve a higher _score_, _greater numbers of comments_ and higher values of other metadata. Moreover, they are very likely to appear in similar network configurations in subreddit named entity networks. This means that they are more often chosen as the results by both the metadata and the network characteristics rankings. Moreover, they are also likely to be crossposted due to their general popularity. Finally, this means that the proposed method works more reliably on non-complex networks, i.e., networks with the high periphery and/or a large number of isolates.