  Signed networks are such social networks having both positive and negative
links. A lot of theories and algorithms have been developed to model such
networks (e.g., balance theory). However, previous work mainly focuses on the
unipartite signed networks where the nodes have the same type. Signed bipartite
networks are different from classical signed networks, which contain two
different node sets and signed links between two node sets. Signed bipartite
networks can be commonly found in many fields including business, politics, and
academics, but have been less studied. In this work, we firstly define the
signed relationship of the same set of nodes and provide a new perspective for
analyzing signed bipartite networks. Then we do some comprehensive analysis of
balance theory from two perspectives on several real-world datasets.
Specifically, in the peer review dataset, we find that the ratio of balanced
isomorphism in signed bipartite networks increased after rebuttal phases.
Guided by these two perspectives, we propose a novel Signed Bipartite Graph
Neural Networks (SBGNNs) to learn node embeddings for signed bipartite
networks. SBGNNs follow most GNNs message-passing scheme, but we design new
message functions, aggregation functions, and update functions for signed
bipartite networks. We validate the effectiveness of our model on four
real-world datasets on Link Sign Prediction task, which is the main machine
learning task for signed networks. Experimental results show that our SBGNN
model achieves significant improvement compared with strong baseline methods,
including feature-based methods and network embedding methods.
