  Networked data, in which every training example involves two objects and may
share some common objects with others, is used in many machine learning tasks
such as learning to rank and link prediction. A challenge of learning from
networked examples is that target values are not known for some pairs of
objects. In this case, neither the classical i.i.d.\ assumption nor techniques
based on complete U-statistics can be used. Most existing theoretical results
of this problem only deal with the classical empirical risk minimization (ERM)
principle that always weights every example equally, but this strategy leads to
unsatisfactory bounds. We consider general weighted ERM and show new universal
risk bounds for this problem. These new bounds naturally define an optimization
problem which leads to appropriate weights for networked examples. Though this
optimization problem is not convex in general, we devise a new fully
polynomial-time approximation scheme (FPTAS) to solve it.
