HDMI：高阶深度复用信息最大值
HDMI: High-order Deep Multiplex Infomax
摘要：
网络已被广泛用于表示对象之间的关系，如学术网络和社交网络，网络的学习嵌入因此获得了大量的研究关注。自监督网络表示学习的目的是在没有外部监督的情况下提取节点嵌入。最近，最大化本地节点嵌入和全局摘要之间的互信息（例如，深度图Infomax，或简称DGI）已经在许多下游任务（如节点分类）上显示出有希望的结果。然而，DGI有两个主要的局限性。首先，DGI仅考虑外部监督信号（即，节点嵌入和全局概要之间的互信息）而忽略固有信号（即，节点嵌入和节点属性之间的相互依赖性）。其次，现实网络中的节点通常由具有不同关系的多条边连接，而DGI没有充分探索节点之间的各种关系。为了解决上述问题，我们提出了一种新的框架，称为高阶深度复用Infomax（HDMI），用于以自监督的方式在复用网络上嵌入学习节点。更具体地说，我们首先通过高阶互信息设计包含外在和内在互信息的联合监督信号，并且我们提出高阶深度信息最大值（HDI）来优化所提出的监督信号。然后，我们提出了一个基于注意力的融合模块，结合节点嵌入从不同层的多路复用网络。最后，我们评估了所提出的HDMI在各种下游任务，如无监督聚类和监督分类。实验结果表明，HDMI在这些任务上达到了最先进的性能。
英文摘要：
Networks have been widely used to represent the relations between objects such as academic networks and social networks, and learning embedding for networks has thus garnered plenty of research attention. Self-supervised network representation learning aims at extracting node embedding without external supervision. Recently, maximizing the mutual information between the local node embedding and the global summary (e.g. Deep Graph Infomax, or DGI for short) has shown promising results on many downstream tasks such as node classification. However, there are two major limitations of DGI. Firstly, DGI merely considers the extrinsic supervision signal (i.e., the mutual information between node embedding and global summary) while ignores the intrinsic signal (i.e., the mutual dependence between node embedding and node attributes). Secondly, nodes in a real-world network are usually connected by multiple edges with different relations, while DGI does not fully explore the various relations among nodes. To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way. To be more specific, we first design a joint supervision signal containing both extrinsic and intrinsic mutual information by high-order mutual information, and we propose a High-order Deep Infomax (HDI) to optimize the proposed supervision signal. Then we propose an attention based fusion module to combine node embedding from different layers of the multiplex network. Finally, we evaluate the proposed HDMI on various downstream tasks such as unsupervised clustering and supervised classification. The experimental results show that HDMI achieves state-of-the-art performance on these tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
