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.

本文提出了一种基于平衡理论的有向图卷积神经网络模型，用于在带有正负连接的有向图中进行节点表示学习，并证明其在 link sign prediction 等问题上的有效性。

有符号图卷积网络

Signed Graph Convolutional Network

Detecting communities in complex networks accurately is a prime challenge,
preceding further analyses of network characteristics and dynamics. Until now,
community detection took into account only positively valued links, while many
actual networks also feature negative links. We extend an existing Potts model
to incorporate negative links as well, resulting in a method similar to the
clustering of signed graphs, as dealt with in social balance theory, but more
general. To illustrate our method, we applied it to a network of international
alliances and disputes. Using data from 1993--2001, it turns out that the world
can be divided into six power blocs similar to Huntington's civilizations, with
some notable exceptions.

本研究扩展了现有的 Potts 模型以包括负链接，将类似于社交平衡理论中的带符号图聚类的方法推广到更广泛的情形，应用于国际联盟和冲突网络，结果表明世界可以分为六个大国区，有一些显著的例外。