KDD2013

The availability and affordability of large-scale data processing is transforming graph mining into a core production use case, especially in the consumer web space. At LinkedIn, the largest professional online social network with 225 million members, a crucial characteristic is the use of static and temporal network features for many applications, particularly recommendations. These include "People You May Know", a link prediction system to find other members on the network; "Endorsements", a lightweight skill reputation product; "Related Searches", query recommendations in our search engine; and more. How do we perform this graph mining at scale? What are some of the challenges we face? Besides the social graph, what about other interesting, but potentially more complex and larger graphs? In this talk, I will illustrate several of LinkedIn's solutions in large scale graph mining.