Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Such network embedding methods are largely focused on finding distributed representations for unsigned networks and are unable to discover embeddings that respect polarities inherent in edges. We propose SIGNet, a fast scalable embedding method suitable for signed networks. Our proposed objective function aims to carefully model the social structure implicit in signed networks by reinforcing the principles of social balance theory. Our method builds upon the traditional word2vec family of embedding approaches and adds a new targeted node sampling strategy to maintain structural balance in higher-order neighborhoods. We demonstrate the superiority of SIGNet over state-of-the-art methods proposed for both signed and unsigned networks on several real world datasets from different domains. In particular, SIGNet offers an approach to generate a richer vocabulary of features of signed networks to support representation and reasoning.

翻译：最近在文字嵌入和文件嵌入方面取得的成功激励了研究人员探索网络的类似表述,并利用这些表述开展边缘预测、节点标签预测和社区探测等任务。这种网络嵌入方法主要侧重于为未签名网络找到分布式表述,无法发现尊重边缘固有的极点的嵌入。我们提议了Signet,这是一个适合签名网络的快速可缩放嵌入方法。我们拟议的目标功能旨在通过加强社会平衡理论的原则,仔细模拟已签署网络中隐含的社会结构。我们的方法以传统的单词2vec 嵌入方法家族为基础,并增加了新的有针对性的节点采样战略,以维持较高等级社区的结构平衡。我们展示了Signet优于为签名和未签名的网络所提议在不同领域的若干真实世界数据集中采用的最新方法。特别是,Signet提供了一种方法,以产生更丰富的签名网络特征词汇支持代表性和推理。