## Appendix A Step-by-Step Example  
To better understand the proposed approach, let us consider a detailed example. This example uses artificial data to highlight key aspects of the proposed method. Moreover, to simplify the description, only _score_ and the _node degree_ are considered.  
Let us assume that there are two subreddits: r/technology (T) and r/programming (P) with posts represented in Tables 1 and 2. Moreover, the only available metadata are the post's _score_. For readability, the named entities are marked in italic.  
During preprocessing, post \(T5\) is deleted, since its _score_ is lower than the threshold (2). The \"[deleted]\" tag means that post \(P4\) was deleted by the user. Hence, it is also removed.  
Next, named entities are extracted from the posts, with the results represented in Table A3.  
Then, using entities and their metadata, two graph networks are created. The entities are mapped into nodes, and the sum of _scores_ of posts they appeared in is calculated.  
Naturally, the node network characteristics are added to the node properties after the edges are instantiated. The network for subreddit r/technology is presented in Figure A1, while that for r/programming is in Figure A2.  
Finally, graph commonality is established. Entities appearing in networks _Python_, _AWS_ and _Canada_ are retained, while the remaining ones are ignored. In this example, the only two properties are _node score_ and _node degree_. All nodes have their (two) similarity metrics calculated. The results are min-max normalized. The final values of node _scores_ and node degrees are shown in Table A4 for r/technology and Table A5 for r/programming.  
Next, the normalized values from both subreddits are compared by calculating their similarity (here, for the reasons outlined above, as a sum of _score_ and a negative absolute difference of the _node degree_), with the results presented in Table A6. The final outcome are two rankings of common entities according to _score_ and degree.  
While in the general case 10 top entities are used (see the next section), here, there are only three entities. Let us assume that only the top two results are of interest. These are Canada and AWS for _score_ and Python and Canada for _degree measure_.  
Let us now illustrate how this information can be confronted with that brought by the crossposts. Let us assume that the following post from r/technology was crossposted to r/programming:  
_Post TP1_  
_Title: Python_ crowned as the most versatile language!  
_Text:_ You can do anything in _Python_! From backend development, through machine learning to embedded systems. See more in this report: (...).  
_Score:_ 13  
Here, the only named entity that matches these captured in both subreddits is _Python_. However, degree-based ranking and _score_-based rankings may not agree. The node degree-based approach pointed to _Python_ and _Canada_ as the two top ranked common entities (as this includes _Python_). On the other hand, the _score_-based top two are _Canada_ and _AWS_. This means that, from this perspective, _Python_ is unimportant.  
Obviously, if the top three (or more, in the general case) common entities were selected, the two approaches would be consistent in terms of results.