Proximity-aware heterogeneous information network embedding
Proximity-aware heterogeneous information network embedding
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DOI：
10.1016/j.knosys.2019.105468
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发表时间：
2020-04-06
影响因子：
8.8
通讯作者：
Pan, Ke
中科院分区：
文献类型：
--
作者：
Zhang, Chen;Wang, Guodong;Pan, Ke
网络嵌入的目的是为网络中的每个节点学习一个高质量的低维表示，近年来引起了越来越多的关注。异构信息网络是最重要的网络之一，具有不同类型的节点和关系。在过去的几年里，异构信息网络嵌入一直是研究的热点。大多数流行的方法生成一组节点序列，并将它们馈送到无监督特征学习模型中，以获得每个节点的低维向量。然而，这些方法的局限性在于，它们的生成节点序列忽略了不同的关系的不同的重要性，他们忽略了邻近信息的巨大价值，揭示了两个节点是否接近或不网络中。为了解决这些局限性，本文提出了一种新的框架称为邻近感知异构信息网络嵌入（PAHINE）。网络的原生信息是从节点序列中提取的，这些节点序列是通过在概率敏感元标记上行走而产生的。然后，将提取的信息输入深度神经网络，以导出所需的嵌入向量。在四个不同异构网络上的实验结果表明，该方法是有效的，它优于最先进的异构网络嵌入算法。(c)2020 Elsevier B.V.保留所有权利。
Network embedding, which aims to learn a high-quality low-dimensional representation for each node in a network, has attracted increasing attention recently. Heterogeneous information networks, with distinguishing types of nodes and relations, are one of the most significant networks. In the past years, heterogeneous information network embedding has been intensively studied. Most popular methods generate a set of node sequences, and feed them into an unsupervised feature learning model to obtain a low-dimensional vector for each node. However, the limitations of these approaches are that their generative node sequences neglect the different importances of diverse relations and they ignore the great value of proximity information which reveals whether two nodes are close or not in the network. To tackle these limitations, this paper presents a novel framework named Proximity-Aware Heterogeneous Information Network Embedding (PAHINE). The native information of a network is extracted from node sequences, which are generated by walking on a probability-sensitive metagraph. Afterwards, the extracted information is fed into deep neural networks to derive the desired embedding vectors. The experimental results on four different heterogeneous networks indicate that the proposed method is efficient and it outperforms the state-of-the-art heterogeneous networks embedding algorithms. (c) 2020 Elsevier B.V. All rights reserved.