## 2 Pertinent State of the Art of Reddit-Related Research
### Graph Networks  
The second part of the literature review concerns graph networks and their construction and utilization in social media research. Specifically, in the context of this work, after topics are extracted, graph networks can be formed and analyzed from the perspectives of selected aspects of the data [49]. Since in this contribution the relations that are captured are bidirectional, and since their significance varies (as described in Section 4), weighted undirected graphs are of particular interest. First, let us discuss the selected most common metrics used in the majority of graph analysis. Here, let us assume the node of the graph is denoted as \(v\). * Degree--the number of edges connected to a node \(v\). For weighted graphs, the sum of weights of all edges connected to a node \(v\) may also be used. * Degree centrality (normalized)--the fraction of all nodes that a given node \(v\) is connected to. * Average neighbor degree [50]--the average of degrees of all nodes that node \(v\) is connected to: \(\frac{\sum degree(u_{i})}{degree(v)}\), where \(u_{i}\) is the neighbors of \(v\). * Clustering--the fraction of all possible triangles (a K3 graph, a complete graph of Size 3) that could pass through a node \(v\). A triangle is a set of three nodes, which are all connected to each other (i.e., a complete graph of Size 3). The derived formula is \(\frac{2T(v)}{degree(v)(degree)-1}\), where \(T(v)\) is the actual number of triangles that pass through node \(v\)[51]. Let us now shortly outline the relevant graph network methods. There are many applications of building graphs from textual features: from general ontologies [52], modeling the real world with graph relations and knowledge graphs [53] enabling reasoning methods, to particular applications in biology [54] and social graph networks [55]. The last one is of particular interest, provided that the dataset under study is derived from Reddit, which is a social network. In the literature, graph networks modeling social media most commonly apply the user-as-node [56; 57; 58], and community-as-node [59; 60; 61; 62] representations. There, connections (edges) are built on the basis of interactions (users commenting/talking) or common belonging (e.g., users subscribing to the same groups/subreddits). However, in this work, a different approach is undertaken. Specifically, the features (mined from the posts) are used to find similarities between communities. In this context, the closest domain covered in the literature is the work related to broadly understood recommender systems. Therefore, while appreciating the difference, works related to such system were reviewed to find general methods, performance evaluation methods, etc. First, there are works concerned with user recommendation. For example, there is a user-to-user recommendation studied in a work about X (formerly Twitter) [63]. Interestingly, this work is based on application of LDA. In particular, the topics extracted using LDA are used to rank users as potential recommendation targets and to build the final recommendation set. The results were tested for a 2010 dataset of tweets. Depending on the subset of the dataset, the recommending systems achieved 20-50% recall. Since the specific _recall_ metric was not defined, the general definition, \(\frac{True\_Positive}{Positive}\) (also known as hit rate, hit ratio, sensitivity, or power), was assumed. As it is clear from multiple related works, recall is the main method for evaluating rankings of recommendation systems. Additionally, the 2010 contribution claims that \"graph-based methods have high precision, since graph information is known to be a reliable estimator of social influence, but also have low recall due to possible low connectivity\". This claim is further addressed when presenting obtained results in Section 5. Moving closer to the core topic of this work, there are hashtag recommendation systems. Hashtags are conceptually similar to named entities, since they point to a specific phenomenon, person, event, organization, etc.