Presented By O'Reilly and Cloudera
Make Data Work
Feb 17–20, 2015 • San Jose, CA

Economic Insights from LinkedIn's Professional Network

June Andrews (Wise / GE Digital)
2:40pm–3:00pm Friday, 02/20/2015
Connected World
Location: LL21 C/D
Average rating: ****.
(4.17, 6 ratings)

LinkedIn’s network consists of 300M+ professionals and their connections to colleagues, peers, and business contacts. The network has evolved over 11 years to incorporate data from 200+ countries with 1.45M job views per day. LinkedIn’s gargantuan amount of data provides insights into questions that could not be answered without vast amounts of human resources before. We can answer for each country, which industries have the most ties with health care? Some relationships are quite surprising. How many introductions would it take to meet Richard Branson? And more seriously, what types of connections are used to find jobs?

Answering these questions requires some data science finesse both in algorithmic choices and in data management. Algorithms that work for networks of one million nodes, do not work for networks with 300M+ nodes. If we tried to compute the connected components of the network with the typical breadth-first search or disjoint-sets algorithms it could take a year. However, an alternative algorithm designed to run on iterative Hadoop system can compute connected components in hours. Once we can compute answers, we have to ask a second question. Does my data answer the question I think it does?

Photo of June Andrews

June Andrews

Wise / GE Digital

June Andrews is an applied mathematician specializing in social network analysis. She has worked on the Search Algorithm at Yelp and designed algorithms for computing the structure of large networks with Professor John Hopcroft. Currently, June works towards understanding the impact of LinkedIn’s Professional Network both on the global scale and for the individual member. She holds degrees in Applied Mathematics, Computer Science and Electrical Engineering from UC Berkeley and Cornell.