Network embedding technologies that transform the nodes of a network into a low-dimensional vector space have many various potential applications such as node classification, community detection and Internet public opinion analysis and so forth. Most existing approaches for network embedding are calculated by utilizing the topology structure of a target network which simply describes the social relations among nodes. However, another factor derived from the significant “opinions” is usually neglected by those works. In particular, there exist dramatic cases of Internet Public Affair, where many users with the same opinion have no social connections. On top of that, the social network can be very sparse, which is unsuitable for network embedding. Therefore, this paper proposes an efficient approach opinion feature network embedding (OFNE) that combines both social relations and opinion features within a network. OFNE defines an opinion feature edge to preserve the opinion features. Experimental results on real-world datasets across different domains demonstrate that the proposed approach OFNE outperforms the regular social network embedding approaches, especially when the opinions have explicit sentiment orientation.

中文翻译：

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OFNE：意见特征调节网络嵌入框架

将网络节点转化为低维向量空间的网络嵌入技术具有多种潜在应用，如节点分类、社区检测和互联网舆情分析等。大多数现有的网络嵌入方法都是通过利用目标网络的拓扑结构来计算的，该结构简单地描述了节点之间的社会关系。然而，从重要的“意见”中衍生出的另一个因素通常被这些作品所忽视。尤其是互联网公关事件中，很多意见相同的用户没有社交关系。最重要的是，社交网络可能非常稀疏，不适合网络嵌入。所以，本文提出了一种有效的方法意见特征网络嵌入（OFNE），它结合了网络内的社会关系和意见特征。OFNE 定义了一条意见特征边来保留意见特征。在不同领域的真实世界数据集上的实验结果表明，所提出的方法 OFNE 优于常规的社交网络嵌入方法，尤其是当意见具有明确的情感取向时。