Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.

中文翻译：

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通过基于补码的串联融合节点嵌入和不完整属性

学习网络节点表示的网络嵌入在网络分析中起着至关重要的作用，因为它可以执行许多下游学习任务。尽管已经提出了各种网络嵌入方法，但是它们主要是为单个网络方案设计的。本文通过研究融合来自两个不同网络的节点嵌入和不完整属性的问题，来考虑“多网络”场景。为了解决这个问题，我们建议对不完整的属性进行补充，以便通过级联进行数据融合。具体来说，我们首先提出一种简单的归纳方法，其中将属性定义为给定节点嵌入向量的参数函数。然后，我们通过自适应学习邻接图来近似原始网络结构，提出其转导形式。此外，我们还提供了这种转导形式的简化版本。在四个数据集上的实验结果证明了我们方法的优越性。