Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose M ulti-view coll A borative N etwork E mbedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE , an attention -based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

中文翻译：

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多视图协作网络嵌入

现实世界的网络通常存在多个视图，其中每个视图描述了一组公共节点之间的一种类型的交互。例如，在视频共享网络上，当两个用户节点链接在一起时，如果它们在一个视图中有共同喜欢的视频，那么如果它们共享共同的订阅者，它们也可以在另一个视图中链接。与传统的单视图网络不同，多个视图保持不同的语义以相互补充。在本文中，我们提出米多视图一种无聊的ñ网络乙mbedding (MANE)，一种用于学习低维表示的多视图网络嵌入方法。与现有研究类似，MANE 取决于多样性和协作——而多样性使视图能够保持其各自的语义，协作使视图能够协同工作。然而，我们也发现了一种新的形式二阶以前没有探索过的协作，并进一步将其统一到我们的框架中以获得更好的节点表示。此外，由于每个视图通常对不同的节点具有不同的重要性，我们建议 MANE ， 一个注意力- 基于 MANE 的扩展，用于对节点视图重要性进行建模。最后，我们对三个公共的、真实的多视图网络进行了综合实验，结果表明我们的模型始终优于最先进的方法。