MEGAN: A Generative Adversarial Network for Multi-View Network Embedding-猫眼课题宝

DOI：

10.24963/ijcai.2019/489

发表时间：

2019-08

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Yiwei Sun;Suhang Wang;Tsung-Yu Hsieh;Xianfeng Tang;Vasant G Honavar

来自许多真实世界应用的数据可以自然地由多视点网络表示，其中不同的视点编码不同类型的关系(例如，友谊、对音乐的共同兴趣等)。在真实世界的个人或实体之间。迫切需要获得这种多视点网络的低维、信息保持和典型的非线性嵌入的方法。然而，大多数关于多视图学习的工作集中在缺乏网络结构的数据上，而关于网络嵌入的大部分工作主要集中在单视图网络上。在此背景下，我们研究了多视点网络表示学习问题，即构造多视点网络的低维信息保持嵌入问题。具体地，我们研究了一种新的用于多视点网络嵌入的生成性对抗网络(GAN)框架，即MEGAN，其目的是保存来自单个网络视点的信息，同时考虑不同视点之间的连通性(从而考虑不同视点之间的互补性和相关性)。在两个真实世界的多视点数据集上的实验结果表明，使用Megan得到的嵌入在节点分类、链接预测和可视化任务上都优于最新的方法。

Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.

MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

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