计算机科学 ›› 2017, Vol. 44 ›› Issue (5): 272-275.doi: 10.11896/j.issn.1002-137X.2017.05.049

• 人工智能 • 上一篇    下一篇

基于神经网络的异构网络向量化表示方法

吴卫祖,刘利群,谢冬青   

  1. 广东海洋大学信息学院 湛江524088,广东海洋大学信息学院 湛江524088,广州大学计算机科学与教育软件学院 广州510006
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受广东省科技计划项目(2014A020218016 ),国家863项目(2009AA012420)资助

Vectorized Representation of Heterogeneous Network Based on Neural Networks

WU Wei-zu, LIU Li-qun and XIE Dong-qing   

  • Online:2018-11-13 Published:2018-11-13

摘要: 当网络中存在不同类型的对象时,对象与对象之间的关系会变得多种多样,网络的结构也会变得更为复杂。针对网络的异构化问题,提出了一种基于神经网络的异构网络向量化表示方法。针对具有图片和文本两种类型对象的异构网络,采用多层次的卷积网络将图片映射到一个潜在的特征空间,采用全连接的神经网络将文本对象也映射到相同的特征空间。在该特征空间内,图片与图片、文本与文本以及图片和文本之间的相似性采用相同的距离计算方法。在实验中,应用提出的方法进行异构网络的多种应用测试,结果表明提出的方法是有效的。

关键词: 异构网络,神经网络,向量化表示,嵌入式向量

Abstract: When there are different types of objects in the network,the relationships between objects would be various,and the structure of the network would become even more complex.To deal with the problem heterogeneousness of networks,this paper proposed a neural network based vectorization representation method of the heterogeneous networks.For the heterogeneous network with two types of images and text,multi-hierarchical convolution neural network was adopted to map the original image into a latent feature space,and fully-connected neural network was adopted to map the text object into the same latent feature space.In the latent feature space,the similarities between images,texts,and even image and text can be calculated by the same distance computing method.In the experiment,the proposed method is applied to test a variety of applications in heterogeneous networks,and the results show that the proposed method is effective.

Key words: Heterogeneous networks,Neural network,Vectorized representation,Embedded vector

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