Ever since I was young, playing video games was one of my favorite hobbies and it still is today. Over the years I have spent with this hobby, I have played various games that spanned a wide array of genres and have seen certain games come in and out of relevancy over time. And as I observed these patterns, I noticed I was asking myself “What games are currently popular? Why did certain games become relevant/irrelevant?”. As such, I wanted to see how applications of network analysis could help me in my quest to gain a larger perspective of the gaming community.
Luckily, Xiaozhou Li and Boyang Zhang of Tampere University have responded to my desire with their January 2020 study “A Preliminary Network Analysis on Steam Game Tags: Another Way of Understanding Game Genres”. In their study, they examined the largest PC gaming platform known as Steam and their user-defined tagging system. The gist of the system is that users can assign tags that they feel represent a game the best (for example, some might tag the game “Call of Duty” as an “Action” and “Shooter” game) and frequently applied tags will become featured categories for that game. Li and Zhang analyzed this tagging system by building a network of game tags, creating an edge between tags if they both were applied to a game. They then performed community detection to see how tags grouped up into communities and labeled the communities based on highest centrality nodes within each community, as shown by Figure 1.
In terms of content related to CSCC46, this study applies the concepts of community detection and PageRank, although the latter has not been discussed yet as of writing this blog. One interesting thing to note is how Li and Zhang performed community detection. They used a method called the “Louvain method” as opposed to the Girvan-Newman algorithm studied in class. Subsequently, the concept of betweenness also appeared in the study; edges with high betweenness connected game tags that usually are not used together. PageRank was used to evaluate the importance of the game tags within the network, much like how PageRank was originally used to rank search query results in search engines.
So why would this information be interesting? Couldn’t you just load up a site like SteamCharts and see what are the top and trending games? Well, yes, you could. But that doesn’t give as broad of an image. The data that Li and Zhang extracted from their study would be useful for game developers as they can see what games people are playing currently. It can provide insight into what kinds of games are currently hot on the market or if there are any genres that are gaining traction. And while the data quality may have been marred by incorrect tagging, it still shows how the wisdom of crowds still provided data that was relatively accurate. Overall, I feel that if this information were frequently updated, publicly available, and covered a broader scope of platforms, it would be an incredibly useful tool for developers and gamers.
References
Li, Xiaozhou & Zhang, Boyang. (2020). A preliminary network analysis on steam game tags: another way of understanding game genres. 65-73. 10.1145/3377290.3377300.