SIGNet: Scalable Embeddings for Signed Networks
SIGNet: Scalable Embeddings for Signed Networks
复制标题
DOI：
10.1007/978-3-319-93037-4_13
复制
发表时间：
2017-02
期刊：
影响因子：
--
通讯作者：
Mohammad Raihanul Islam;B. Prakash;Naren Ramakrishnan
中科院分区：
文献类型：
--
作者：
Mohammad Raihanul Islam;B. Prakash;Naren Ramakrishnan
最近在单词嵌入和文档嵌入方面的成功促使研究人员探索网络的类似表示，并将这种表示用于边缘预测，节点标签预测和社区检测等任务。这种网络嵌入方法主要集中在寻找无符号网络的分布式表示，并且无法发现尊重边缘中固有极性的嵌入。我们提出了SIGNet，一种适用于签名网络的快速可扩展嵌入方法。我们提出的目标函数旨在通过加强社会平衡理论的原则，仔细建模隐含在签名网络中的社会结构。我们的方法建立在传统的word2vec家族的嵌入方法，并增加了一个新的有针对性的节点采样策略，以保持结构平衡，在高阶社区。我们证明了SIGNet的优越性超过国家的最先进的方法，提出了几个真实的世界数据集从不同领域的签名和未签名的网络。特别是，SIGNet提供了一种方法来生成更丰富的签名网络特征词汇表，以支持表示和推理。
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.