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Tinder: The Ever-Expanding Matchmaking Network

If you live on a college campus, you probably already know what Tinder is. Tinder is a matchmaking application for iOS and Android based on the premise of “swiping.” To date there are 750 million “swipes” per day. If you see a profile, and that profile sees you, and you both “swipe right” on each other, you are then connected. If one or both users swipe left, this means that they are not interested, and will not be paired together. This is the foundation of the matchmaking function of the app; a user sees another’s picture, and if they both are attracted to each other, then they become connected. This connection is known, in Tinder terms, as a “match.” In total there are 2 billion matches. The goal behind the app is to eliminate social barriers, and simply acknowledge right away based on someone’s profile whether you want to interact with them. Tinder already has a massive presence on college campuses, and it is ever growing.

The algorithm behind the app relies on the user’s location, Facebook settings, gender, and more relevant variables. Users can also pick what users they are exposed to. The typical example would be filtering the gender of users you are exposed to (men to women, women to men, etc.). One can only speculate on the actual mechanics of the algorithm. For instance, Tinder is known to match people who are at the same technical level of “attractiveness.” This could be because, in all likelihood, users are ranked on a ratio of right swipes to left swipes, which would be a gauge of their general level of attractiveness. This level is taken into account with the algorithm, and people with the closest ratios, as well as the closest distance, are given the opportunity to pair with each other.

While this concept may seem superficial, it is actually a very interesting study of networking. We can model this app in a graph theory context, looking at users as nodes. The edges can be whether the user is connected or not, along with a magnitude of distance. What is interesting here is how the Strong Triadic Closure principle would, in most cases, not apply. For example, take a cluster of nodes A, B, and C, where A is connected to B and C. Given that the overwhelming majority of Tinder users are heterosexual, and that A is matched with B and C, we can conclude that B and C are most likely of different genders to A. No matter how strong the connection is, how close the attractiveness levels are between the nodes, the settings of most Tinder users would prevent B and C from becoming interconnected, thus violating the principle of Strong Triadic Closure.

The premise of this app is fascinating, both from a sociology standpoint and from a networking standpoint. Were the data behind the users to be open to the public, I would be very curious to analyze the patterns in the data to see which users connect with others. The app itself is continuing to grow with more users becoming connected, and it seems as though there is no end in sight.




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