Structure-guided attributed network embedding with “centroid” enhancement
Structure-guided attributed network embedding with “centroid” enhancement
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DOI：
10.1007/s00607-021-00916-y
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发表时间：
2021-02
期刊：
影响因子：
3.7
通讯作者：
Zihan Liao;Wenxin Liang;Beilei Cui-;Xin Liu
中科院分区：
文献类型：
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作者：
Zihan Liao;Wenxin Liang;Beilei Cui-;Xin Liu
属性网络嵌入旨在学习网络结构和属性信息方面的低维网络表示。大多数现有方法分别处理网络结构和属性，并以特定方式将它们组合起来，这削弱了结构和属性之间的亲和力，从而导致性能不佳。此外，一些方法仅关注局部或全局网络结构，而没有充分利用网络下的结构信息。为了解决这些限制，我们提出了具有“质心”增强的结构引导属性网络嵌入，这是一种全面、无缝地嵌入网络结构和属性信息的无监督方法。具体来说，我们将每个节点的邻域视为一个“簇”，并通过图卷积网络为其计算“质心”。我们设计了一个基于“质心”的三重正则化器，以施加受 K 均值启发的间隙约束。利用“质心”增强skip-gram模型来处理高阶邻近性。通过联合优化两个目标，学习到的表示可以保留局部-全局网络结构和属性信息。在整个模型中，我们利用网络结构来指导属性的聚合，从而有效地捕获它们之间的亲和力。八个真实世界数据集的实验结果证明了我们的模型相对于最先进的方法的优越性。
Attributed network embedding aims at learning low-dimensional network representations in terms of both network structure and attribute information. Most existing methods deal with network structure and attributes separately and combine them in particular ways, which weaken the affinity between structure and attributes and thus lead to suboptimal performance. Moreover, some methods focus solely on local or global network structure, without fully utilizing the structure information underling the network. To address these limitations, we propose structure-guided attributed network embedding with “centroid” enhancement, an unsupervised approach to embed network structure and attribute information comprehensively and seamlessly. Specifically, we regard the neighborhood of each node as a “cluster” and calculate a “centroid” for it through graph convolutional network. We design a “centroid”-based triplet regularizer to impose a gap constraint inspired by K-means. A “centroid”-augment skip-gram model is utilized to deal with high-order proximity. By jointly optimizing the two objectives, the learned representation can preserve both local-global network structure and attribute information. Throughout the model, we exploit network structure to guide the aggregation of attributes, and thus effectively captures the affinity between them. Experimental results on eight real-world datasets demonstrate the superiority of our model over the state-of-the-art methods.