Deep neural networks for learning graph representations

Shaosheng Cao, Wei Lu, Qiongkai Xu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    807 Citations (Scopus)


    In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the samplingbased method for generating linear sequences proposed by Perozzi et al. (2014). The advantages of our approach will be illustrated from both theorical and empirical perspectives. We also give a new perspective for the matrix factorization method proposed by Levy and Goldberg (2014), in which the pointwise mutual information (PMI) matrix is considered as an analytical solution to the objective function of the skipgram model with negative sampling proposed by Mikolov et al. (2013). Unlike their approach which involves the use of the SVD for finding the low-dimensitonal projections from the PMI matrix, however, the stacked denoising autoencoder is introduced in our model to extract complex features and model non-linearities. To demonstrate the effectiveness of our model, we conduct experiments on clustering and visualization tasks, employing the learned vertex representations as features. Empirical results on datasets of varying sizes show that our model outperforms other stat-of-The-Art models in such tasks.

    Original languageEnglish
    Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
    PublisherAAAI Press
    Number of pages8
    ISBN (Electronic)9781577357605
    Publication statusPublished - 2016
    Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
    Duration: 12 Feb 201617 Feb 2016

    Publication series

    Name30th AAAI Conference on Artificial Intelligence, AAAI 2016


    Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
    Country/TerritoryUnited States


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