  Graph neural networks have been widely used for learning representations of
nodes for many downstream tasks on graph data. Existing models were designed
for the nodes on a single graph, which would not be able to utilize information
across multiple graphs. The real world does have multiple graphs where the
nodes are often partially aligned. For examples, knowledge graphs share a
number of named entities though they may have different relation schema;
collaboration networks on publications and awarded projects share some
researcher nodes who are authors and investigators, respectively; people use
multiple web services, shopping, tweeting, rating movies, and some may register
the same email account across the platforms. In this paper, I propose partially
aligned graph convolutional networks to learn node representations across the
models. I investigate multiple methods (including model sharing,
regularization, and alignment reconstruction) as well as theoretical analysis
to positively transfer knowledge across the (small) set of partially aligned
nodes. Extensive experiments on real-world knowledge graphs and collaboration
networks show the superior performance of our proposed methods on relation
classification and link prediction.
