Analyzing Subreddit Networks



The popular website reddit.com contains numerous messageboards (called "subreddits"), each dedicated to a particular subject, such as a hobby or topic of interest. Often the users of these subreddits will place links to related subreddits on the sidebar of the webpage. In this way, these linked subreddits form a network of closely related online communities. Because these lists of links are human-curated, they give high-quality indicators of related topics and communities. By following these links programmatically, we can collect the data needed to to visualize and analyze these networks.

The code for this project can be found on Github.

Scrape the Data

I wrote simple web scraper using the Python library scrapy which collects the list of links and other information from each subreddit page. Starting from an initial subreddit, the scraper searches the sidebar on the page for any related subreddits that have been linked there. The scraper then follows those links and iteratively searches for more links. The name of each subreddit is collected, along with its description, number of subscribers, and the list of links to other subreddits.

From these data, it is possible to construct the network of subreddits surrounding the initial page. In this network (often called a graph in mathematics), each node is a subreddit and each edge is a hyperlink between them. For simplicity, I decided to build a non-directed graph, ignoring the directionality of hyperlinks: a hyperlink from subreddit A to B is treated in the same manner as a hyperlink from subreddit B to A.

The scraper uses CSS selectors to locate the desired elements on each webpage. Here is a code snippet with the selectors:

for sidebar in response.css('div.s1s8pi67-0'):
    sub_count = subscriber_conversion(
                )  # convert from string to numeric
    links = list()
    for link in response.css('div.s1s8pi67-0 a::attr(href)').re(r'/r/\w+'):
    yield {
      'subreddit': re.search(r'/r/\w+', response.request.url).group().lower(),
      'description': sidebar.css('p.s34nhbn-14::text').extract_first(),
      'links': links,
      'subscribers': sub_count

The sidebar on the page is identified using the method response.css('div.s1s8pi67-0') where response is the object representing the returned webpage. The string s1s8pi67-0 is a unique class of the div element containing the sidebar. [1] The scaper returns the name of the subreddit, the subreddit's description, the list of links in the sidebar, and the number of subscribers.

I chose two subreddits to use as the starting points to crawl through related pages, which results in two networks for analysis:

  1. one centered at /r/programming, which I will refer to as the "programming network"
  2. one centered at /r/financialindependence, which I will refer to as the "financial network."

Both of these initial subreddits have a fair number of links listed in their sidebars, which should lead to larger, more complex networks.

[1]Using these seemingly random attribute values for the selectors is less than ideal: they are non-semantic and they seem to change fairly frequently, possibly with each build of the website. An improvement woudl be to find a more robust selector. I think it would be possible to use an XPath selector to find the text /r/[subreddit name] that appears in the sidebar, and then select the document element containing this a few steps up the hierarchy.


After collecting the network data, we can use the library networkx to visualize and analyze the networks. I quickly made a couple plots using matplotlib to visualize the graphs.

network centered as /r/programming

Unfortunately, these were difficult to read and not very useful for exploring the networks. To remedy this, I decided to use the visualization library d3 (written in JavaScript) to make some interactive plots. After we convert the network data into the "node-link" JSON format using networkx, we can read it into a HTML file containing JavaScript visualizations.

Click on the images below to view the interactive plots.

network centered as /r/financialindependence

Plot of the network centered at the subreddit /r/financialindependence

network centered as /r/programming

Plot of the network centered at the subreddit /r/programming

In these charts, the relative number of subscribers to each subreddit is represented by the radius of the node (using a log-scale).

One of the most salient features is the "spoke-and-hub" structure: a larger subreddit links to many smaller subreddits, which are often dedicated to a more specific topic. For example, /r/financialindependence is linked to country-specific subreddits for Canada (/r/personalfinancecanada), UK (/r/ukpersonalfinance), and so on. These can often be sensibly grouped into a cluster of nodes based on their subject matter. An example of this is the closely related subreddits surrounding /r/collapse which are all dedicated to the topic of societal and economic collapse.


Average Degree and Density

One descriptive statistic of a graph is the average degree. The degree of a node is the number of edges connected to it. The average degree of a graph is simply the average of the degrees of each of its nodes. These two networks have low average degrees, both around 1.3. This is a consequence of the structure of these networks: there are a small number of "hub" nodes that have links to a large number of "spoke" nodes, which have few links. As a result, most of the nodes are "spoke" nodes, usually only having a single edge. This lack of connections is also shown in another metric, the graph density.

Graph density is defined as the ratio of the number of edges to the total possible number of edges between the nodes. The total possible would be achieved if every node was connected to every other node. For a graph with n nodes, this would result in n choose 2 or n * (n - 1) / 2 edges. The density thus varies from 0 (in a graph with no edges) to 1 (in a graph with every possible edge). The densities of the financial and programming graphs are 0.01 and 0.04, respectively, so they have low density.


Using networkx, we can also calculate metrics which helps us to better understand the network. One property of nodes in a network that we are interested in is their centrality. The metric of betweenness centrality is one way of calculating this. The betweenness centrality of a node is the proportion of shortest paths between any other two nodes that pass through it. "Spoke" nodes will have low values and "hubs" high values.

The most central nodes in the financial network are:

Subreddit Betweenness Centrality Edges
/r/frugal 0.63 27
/r/buildapc 0.48 11
/r/collapse 0.31 35
/r/gamedeals 0.29 14
/r/simpleliving 0.26 18
/r/canadianhardwareswap 0.24 14
/r/zerowaste 0.19 16
/r/meditation 0.17 9
/r/steam 0.14 10
/r/buildapcsales 0.12 4
/r/financialindependence 0.12 15

/r/frugal and /r/buildapc are central because they act as a bridge between the network's two main branches: one focused on financial matters and the other focused on computer building and gaming. Because of this, many shortest paths must pass through them. /r/frugal also unites the main hubs in the financial branch, /r/collapse, /r/zerowaste, /r/simpleliving, and /r/financialindependence.

/r/collapse is a hub for many small subreddits that are not linked to any other nodes. Any path from one of these nodes to another other must necessarily pass through /r/collapse, contributing to its high centrality.


Another metric for describing a network is the clustering coefficient. Before we define this, first define a triangle as a sub-graph of three nodes that are all connected to each other. Suppose we have a node u with degree n. The maximum possible of triangles including u is n choose 2, or n * (n - 1) / 2. The clustering coefficient is the number of existing triangles including node u divided by this maximum possible number. So, this coefficient will always be between 0 and 1. It can be interpreted as the tendency of a node to cluster with other nodes. Any node that is only connected to a single other node will always have a clustering coefficient of 0. If all of a node's neighboring nodes are connected, then the node will have a clustering coefficient of 1.

Most of the nodes in our two networks are spokes connected only to a single hub node and thus will have a clustering coefficient of 0. Nodes with coefficients significantly larger than 0 are more rare in these networks. This is perhaps not surprising given that these are sparse graphs.

The nodes in the programming network with the highest clustering coefficients are:

Subreddit Clustering Coefficient Edges
/r/programmerhumor 1.00 2
/r/cseducation 1.00 2
/r/computerscience 1.00 2
/r/cryptocurrencymemes 1.00 2
/r/compsci 1.00 3
/r/freelance 1.00 2
/r/cs_questions 1.00 2
/r/resumes 1.00 2
/r/coding 1.00 3
/r/javascript 1.00 2
/r/experienceddevs 1.00 2
/r/learnprogramming 0.67 4
/r/jobs 0.33 3

Many of these have only two or three few neighbors, so the clustering coefficient of 1 is less significant. In contrast, while /r/csmajors has a coefficient of only 0.17, it has 12 neighbors: out of the 66 possible triangles, 11 of them are fully connected. This subreddit be part of something closer to a cluster than many of nodes with a clustering coefficient of 1.

Next Steps

There is much room for expansion on this sort of analysis. Some further avenues to explore are:

1. A more extensive network could be constructed by crawling the actual posts on each messageboard and collecting hyperlinks given there. Links to webpages outside of reddit.com could also be crawled.

2. The number of links between webpages could be tabulated in order to measure the strength of each link in the network.

3. Instead of an undirected graph, the direction of the links could be incorporated into the model.