Dynamic Heterogeneous Network Representation Method Based on Meta-Path

被引：0

作者：

Liu Q. ^{[1
]}

Tan H.-S. ^{[1
]}

Zhang Y.-M. ^{[1
]}

Wang G.-Y. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2022年 / 50卷 / 08期

关键词：

attention mechanism; dynamic heterogeneous network; gated recurrent unit; metapath; network representation learning;

D O I：

10.12263/DZXB.20211288

中图分类号：

学科分类号：

摘要：

The researches of network representation learning have made many achievements. Since most of the researches ignore the dynamics and heterogeneity of the networks, coupled temporal and spatial structure features can not be distinguished, and rich semantic information of the network cannot be captured well. In this paper, meta-path based dynamic heterogeneous network representation learning method is proposed. Firstly, the neighborhood structures of nodes are divided into different sub-spaces according to their time, then the sequences of all time-weighted meta-paths for each node are sampled. Secondly, the neighborhood information on all time-weighted meta-paths of each node is integrated by a gated recurrent unit network(GRU). Furthermore, a bi-directional gated recurrent unit network(Bi-GRU) with an attention mechanism is used to learn the spatio-temporal contextual information from the merged sequences, and the final node representation will be received. Experiments on real data sets show that our algorithm has greatly improved performance on the downstream network tasks, such as node classification, clustering and visualization. Compared with state-of-the-art baseline methods,the Micro-F1 value has been raised by 1.09%~3.72% averagely on classification tasks, and the ARI value has been increased by 3.23%~14.49% on clustering tasks. © 2022 Chinese Institute of Electronics. All rights reserved.

引用

页码：1830 / 1839

页数：9

共 24 条

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