  Due to the fact much of today's data can be represented as graphs, there has
been a demand for generalizing neural network models for graph data. One recent
direction that has shown fruitful results, and therefore growing interest, is
the usage of graph convolutional neural networks (GCNs). They have been shown
to provide a significant improvement on a wide range of tasks in network
analysis, one of which being node representation learning. The task of learning
low-dimensional node representations has shown to increase performance on a
plethora of other tasks from link prediction and node classification, to
community detection and visualization. Simultaneously, signed networks (or
graphs having both positive and negative links) have become ubiquitous with the
growing popularity of social media. However, since previous GCN models have
primarily focused on unsigned networks (or graphs consisting of only positive
links), it is unclear how they could be applied to signed networks due to the
challenges presented by negative links. The primary challenges are based on
negative links having not only a different semantic meaning as compared to
positive links, but their principles are inherently different and they form
complex relations with positive links. Therefore we propose a dedicated and
principled effort that utilizes balance theory to correctly aggregate and
propagate the information across layers of a signed GCN model. We perform
empirical experiments comparing our proposed signed GCN against
state-of-the-art baselines for learning node representations in signed
networks. More specifically, our experiments are performed on four real-world
datasets for the classical link sign prediction problem that is commonly used
as the benchmark for signed network embeddings algorithms.
