  It is generally accepted that neighboring nodes in financial networks are
negatively assorted with respect to the correlation between their degrees. This
feature would play an important 'damping' role in the market during downturns
(periods of distress) since this connectivity pattern between firms lowers the
chances of auto-amplifying (the propagation of) distress. In this paper we
explore a trade-network of industrial firms where the nodes are suppliers or
buyers, and the links are those invoices that the suppliers send out to their
buyers and then go on to present to their bank for discounting. The network was
collected by a large Italian bank in 2007, from their intermediation of the
sales on credit made by their clients. The network also shows dissortative
behavior as seen in other studies on financial networks. However, when looking
at the credit rating of the firms, an important attribute internal to each
node, we find that firms that trade with one another share overwhelming
similarity. We know that much data is missing from our data set. However, we
can quantify the amount of missing data using information exposure, a variable
that connects social structure and behavior. This variable is a ratio of the
sales invoices that a supplier presents to their bank over their total sales.
Results reveal a non-trivial and robust relationship between the information
exposure and credit rating of a firm, indicating the influence of the neighbors
on a firm's rating. This methodology provides a new insight into how to
reconstruct a network suffering from incomplete information.
