Multi-Layered Network Embedding
Multi-Layered Network Embedding
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DOI：
10.1137/1.9781611975321.77
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Jundong Li; Chen-Chen-Chen;Hanghang Tong;Huan Liu
中科院分区：
文献类型：
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作者：
Jundong Li; Chen-Chen-Chen;Hanghang Tong;Huan Liu
近年来，网络嵌入得到了越来越多的关注。已经证明，学习的低维节点向量表示可以推进无数的图挖掘任务，如节点分类，社区检测和链接预测。绝大多数现有的电子竞技都致力于单层网络或具有单一类型的节点和节点交互的同构网络。然而，在许多现实世界的应用中，各种网络可以抽象并以多层方式呈现。典型的多层网络包括关键基础设施系统、协作平台、社交推荐系统等。尽管多层网络被广泛使用，但由于层内连接和跨层网络依赖性的令人困惑的组合，学习不同类型节点的矢量表示仍然是一项艰巨的任务。本文研究了一个新的多层网络嵌入问题。特别是，我们提出了一个原则性的框架-马内，在一个统一的艾德优化框架中同时对层内连接和跨层网络依赖进行建模，用于嵌入表示学习。在实际多层网络上的实验证实了该框架的有效性。
Network embedding has gained more attentions in recent years. It has been shown that the learned low-dimensional node vector representations could advance a myriad of graph mining tasks such as node classifi-cation, community detection, and link prediction. A vast majority of the existing efforts are overwhelmingly devoted to single-layered networks or homogeneous networks with a single type of nodes and node interactions. However, in many real-world applications, a variety of networks could be abstracted and presented in a multi-layered fashion. Typical multi-layered networks include critical infrastructure systems, collaboration platforms, social recommender systems, to name a few. Despite the widespread use of multi-layered networks, it remains a daunting task to learn vector representations of different types of nodes due to the bewildering combination of both within-layer connections and cross-layer network dependencies. In this paper, we study a novel problem of multi-layered network embedding. In particular, we propose a principled framework - MANE to model both within-layer connections and cross-layer network dependencies simultaneously in a unified optimization framework for embedding representation learning. Experiments on real-world multi-layered networks corroborate the effectiveness of the proposed framework.