Social Graphs are becoming more and more crucial to online dating, as dating companies discover how much more accurate their recommendations become when considering the network effects.

When it comes to dating, everybody is highly motivated. So it is no surprise that the nerdy among us put their advanced knowledge to work when seeking out a mate. The most recent celebrated example is Chris McKinlay, who used a statistical modeling approach to find which type of women to go after. The result: after 88 dates, McKinlay found the right woman for him, who, as it turns out, had been hacking her profile in a different way (see “How a Math Genius Hacked OkCupid to Find True Love”). But interest in applying technology to find love is also highlighting a shift toward graph database technology that is starting to transform applications in a large number of industries. Here is the evidence: Several of the largest dating sites in the world have shifted toward graph databases in the last nine months.
  • LinkedIn has a large team working on a proprietary graph database, which sits at the center of nearly every operation at LinkedIn.
  • Twitter depends on a graph database, and has released FlockDB, a graph database it created, as open source.
  • Neo Technology, the creator of Neo4j, the most popular graph database, now has more than 30 Global 2000 companies adopt its technology, including enterprise brands like Wal-Mart, eBay, Lufthansa, and Deutsche Telekom.
  • Teradata just released a new type of SQL called SQL-GR, intended to make the graph analytics easy for enterprise users.
  • According to a report by industry observer DB-Engines, “Graph DBMSs are gaining in popularity faster than any other database category,” growing 300 percent since January of last year.
It seemed appropriate to use Valentines Day and online dating as an opportunity to explore why graph databases are increasingly powering the search for love, as well as what the lessons are for other sorts of applications. It’s the Relationships, Stupid! Social Graphs are becoming more and more crucial to online dating, as dating companies discover how much more accurate their recommendations become when considering the network effects. Snap Interactive, the company behind the dating site AYI – are you interested?, uses a one billion person social graph to significantly improve the likelihood of finding a match. It does this by using the graph to recommend people in one’s extended social network: friends-of-friends, and friends-of-friends-of-friends, who statistically speaking are much more likely to go out on a date than complete strangers. In just the last six months, more than half a dozen online dating companies around the world have quietly implemented graph databases to help them bring the power of the network into their decision-making. Key graphs include not just the social graph, but also the passion graph (of shared interests), location graph, and others. Glassdoor, which is for careers and jobs what Yelp is for food, accomplishes much the same thing, but with companies, jobs, and job seekers, also with a graph of nearly a billion people, consisting of its users and their friends. Both Snap and Glassdoor report they have significantly improved the accuracy of their recommendations by using a graph to navigate their connected data. By finding and making better use of networks, many different types companies are breaking new ground with respect to intelligent real-time analytics. In his session at Strata, Eifrem, CEO of Neo Technology, reported that many people, once they learn what a graph database can do, start seeing graphs absolutely everywhere. Treating relationships, sometimes called the edges of a graph, as a first class object is the fundamental innovation of graph databases. The database doesn’t only store just information about individual things, but it also stores the relationships between those things. This capability makes it much easier to express sophisticated questions, and get answers in a small fraction of the time it takes a traditional database. The relationships in the database can express the nature of each connection (parent, child, owns, friend) and capture any number of qualitative or quantitative facts about that relationship (weighting, start and end date, etc.). Read the full article.  

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