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Decoupled Variational Embedding for Signed Directed Networks
ACM Transactions on the Web ( IF 3.5 ) Pub Date : 2020-10-28 , DOI: 10.1145/3408298
Xu Chen 1 , Jiangchao Yao 1 , Maosen Li 1 , Ya Zhang 1 , Yanfeng Wang 1
Affiliation  

Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification, and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies mainly focus on preserving the first-order network topology that indicates the closeness relationships of nodes. However, these methods generally fail to capture the high-order topology that indicates the local structures of nodes and serves as an essential characteristic of the network topology. In addition, for the first-order topology, the additional value of non-existent links is largely ignored. In this article, we propose to learn more representative node embeddings by simultaneously capturing the first-order and high-order topology in signed directed networks. In particular, we reformulate the representation learning problem on signed directed networks from a variational auto-encoding perspective and further develop a decoupled variational embedding (DVE) method. DVE leverages a specially designed auto-encoder structure to capture both the first-order and high-order topology of signed directed networks, and thus learns more representative node embeddings. Extensive experiments are conducted on three widely used real-world datasets. Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE. Qualitative results and analysis are also given to provide a better understanding of DVE.

中文翻译:

有向有向网络的解耦变分嵌入

符号有向网络的节点表示学习在许多实际应用中受到了相当大的关注,例如链接符号预测、节点分类和节点推荐。挑战在于如何充分编码网络的复杂拓扑信息。最近的研究主要集中在保存第一个订单表示节点之间紧密关系的网络拓扑。但是,这些方法通常无法捕获高阶拓扑表示节点的局部结构,是网络拓扑的基本特征。此外,对于第一个订单拓扑结构中,不存在的链接的附加价值在很大程度上被忽略了。在本文中,我们建议通过同时捕获第一个订单高阶有符号有向网络中的拓扑。特别是,我们从变分自动编码的角度重新表述了有向有向网络的表示学习问题,并进一步开发了一种解耦变分嵌入(DVE)方法。DVE 利用专门设计的自动编码器结构来捕获第一个订单高阶有符号有向网络的拓扑结构,从而学习更多具有代表性的节点嵌入。在三个广泛使用的真实世界数据集上进行了广泛的实验。链路符号预测和节点推荐任务的综合结果证明了 DVE 的有效性。还给出了定性结果和分析,以更好地理解 DVE。
更新日期:2020-10-28
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