I haven’t talked much about TunkRank in the past months, largely because Jason Adams, who stepped up to the TunkRank Implementation Challenge last year, has been leading the charge. Indeed, all I did, beyond lending my first syllable to its name, was to propose the measure and get it implemented “Tom Sawyer” style.
- TunkRank was cited in a WSDM 2010 paper entitled “TwitterRank: finding topic-sensitive influential twitterers“
- Nicolas Cerrato implemented an influence measure for gamer site Gamocracy based on TunkRank.
And, most recently:
- University of Oviedo professor Daniel Gayo-Avello published a research paper entitled “Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms“, based on a follower graph of 1.8M Twitter users, in which he reports:
Lastly, there are one method clearly outperforming PageRank with respect to penalization of abusive users while still inducing plausible rankings: TunkRank. It is certainly similar to PageRank but it makes a much better job when confronted with “cheating”: aggressive marketers are almost indistinguishable from common users –which is, of course, desirable; and spammers just manage to grab a much smaller amount of the global available prestige and reach lower positions –although they still manage to be better positioned than average users. In addition to that, the ranking induced by TunkRank certainly agrees with that of PageRank, specially at the very top of the list, meaning that many users achieving good positions with PageRank should also get good positions with TunkRank. Thus, TunkRank is a highly recommendable ranking method to apply to social networks: it is simple, it induces plausible rankings, and severely penalizes spammers when compared to PageRank.
You can read a summary version in his blog post, descriptively titled “Research on a 1.8M Twitter user graph. Conclusion: TunkRank is your best option.“
I’ve excited that an idea I came up with on a whim (or perhaps out of excessive idealism) has taken such a life of its own. And hey, I do work for a company that is into real-time search and that knows a thing or two about adversarial information retrieval. Hopefully I’ll find way to apply TunkRank–or at least its intuition–in my own work. In the mean time, I offer those who have already done so my congratulations and gratitude.