  Nowadays small and medium-sized enterprises have become an essential part of
the national economy. With the increasing number of such enterprises, how to
evaluate their credit risk becomes a hot issue. Unlike big enterprises with
massive data to analyze, it is hard to find enough information of small
enterprises to assess their financial status. Limited by the lack of primary
data, how to inference small enterprises' credit risk from secondary data, like
information of their upstream, downstream, parent, and subsidiary enterprises
attracts big attention from industry and academy. Targeting on accurately
evaluating the credit risk of the small and medium-sized enterprise (SME), in
this paper, we exploit the representative power of Information Network on
various kinds of SME entities and SME relationships to solve the problem. A
novel feature named meta path feature proposed to measure the credit risk,
which makes us able to evaluate the financial status of SMEs from various
perspectives. Experiments show that our method is effective to identify SMEs
with credit risks.
