  Yelp is one of the largest online searching and reviewing systems for kinds
of businesses, including restaurants, shopping, home services et al. Analyzing
the real world data from Yelp is valuable in acquiring the interests of users,
which helps to improve the design of the next generation system. This paper
targets the evaluation of Yelp dataset, which is provided in the Yelp data
challenge. A bunch of interesting results are found. For instance, to reach any
one in the Yelp social network, one only needs 4.5 hops on average, which
verifies the classical six degree separation theory; Elite user mechanism is
especially effective in maintaining the healthy of the whole network; Users who
write less than 100 business reviews dominate. Those insights are expected to
be considered by Yelp to make intelligent business decisions in the future.
