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We show the latent factors of Dirichlet Graph Variational Autoencoder can be understood as cluster memberships and the reconstruction term connects with spectral relaxed balanced graph cut

Dirichlet Graph Variational Autoencoder

NIPS 2020, (2020)

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Abstract

Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation of what these latent factors are and why they perform well. In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster members...More

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Introduction
  • Since the introduction of Graph Neural Networks (GNNs) [19, 4, 6] and Variational Autoencoders (VAEs) [16], many studies [18, 21, 8] have used GNNs and VAEs (GVAEs) to generate realistic graphs with latent factors.
  • Inspired by the recent development of variational autoencoder topic model [27, 3] in text generation, in this work, the authors propose to formulate the latent factors in GVAEs as graph cluster memberships, analogous to topics in text generation.
  • JT-VAE [14] proposes to generate molecular graphs in two phases, in which it first
Highlights
  • Since the introduction of Graph Neural Networks (GNNs) [19, 4, 6] and Variational Autoencoders (VAEs) [16], many studies [18, 21, 8] have used GNNs and VAEs (GVAEs) to generate realistic graphs with latent factors
  • As an ablation study, when replacing Heatts with Graph Convolutional Networks (GCN) [19], the performance is just comparable to baselines, and worse than Dirichlet Graph Variational Autoencoder (DGVAE), which shows the superiority of Heatts
  • As DGVAE/DGAE does not rely on K-means to derive cluster memberships, this cluster performance indicates the effectiveness of our framework on graph clustering tasks
  • We show the latent factors of DGVAE can be understood as cluster memberships and the reconstruction term connects with spectral relaxed balanced graph cut
  • Motivated by low pass characteristics in balanced graph cut, we propose Heatts, a new variant of GNN, which utilizes Taylor series for the fast computation of heat kernels and admits better low pass characteristics than GCN
Methods
  • Data and baselines The authors follow Graphite [8] and create data sets from six graph families with fixed, known generative processes, to evaluate the performance of DGVAE on graph generation.
  • The authors compare with GAE/VGAE [18] and Graphite-AE/VAE [8].
  • Setup For DGVAE/DGAE, the authors use the same network architecture through all the experiments.
  • The authors train for 200 iterations with a learning rate of 0.01.
  • The Dirichlet prior is set to be 0.01 for all dimensions if not specified otherwise.
Results
  • The negative log-likelihood (NLL) and root mean square error (RMSE) on a test set of instances are shown in Table 1
  • Both DGVAE and DGAE outperform their competitors significantly on all data sets, indicating the effectiveness of DGVAE and DGVE.
  • The clustering accuracy (ACC), normalized mutual information (NMI) and macro F1 score (F1) are shown in Table 2
  • Both DGVAE and DGAE outperform their competitors on most data sets.
  • As an ablation study, when replacing Heatts with GCN [19], the performance is just comparable to baselines, and worse than DGVAE, which again proves the superiority of Heatts over GCN
Conclusion
  • The authors present DGVAE, a graph variational generative model with Dirichlet latent variables.
  • The authors show the latent factors of DGVAE can be understood as cluster memberships and the reconstruction term connects with spectral relaxed balanced graph cut.
  • The effectiveness of DGVAE is validated on graph generation and graph clustering tasks.
  • This work connects VAEs based graph generation and traditional graph research topic — balanced graph cut.
  • Researchers in drug design or molecule generation may benefit from this research, since the interpretation of deep learning based graph generation is worthwhile to be further explored
Tables
  • Table1: Test graph generation comparison of different methods
  • Table2: Cluster performance comparison of different methods
  • Table3: Statistics of data sets used in graph clustering
Download tables as Excel
Related work
  • Dirichlet VAEs Previous studies [3, 27, 32, 15] on VAEs have enabled the usage of the Dirichlet distributions as priors, and most of them are applied in the text domain. In these practices, there are two commonly observed difficulties: (1) reparameterization trick is problematic when the Dirichlet distributions are applied [27], and (2) component collapsing, in which the model reaches close to the prior belief [17]. To tackle these two issues, Srivastava and Sutton [27] resolve the former by softmax Laplace approximation [12] and the latter by stacking training strategies, i.e., higher learning rate, batch normalization and dropout. Joo et al [15] use inverse Gamma approximation to address the former issue and argue there is no component collapsing in their modeling. Burkhardt and Kramer [3] apply rejection sampling variational inference for the former and propose sparse Dirichlet VAEs to address the latter. Some other methods include Weibull distribution approximation [35] and Dirichlet stick-breaking priors [22].
Funding
  • Acknowledgments and Disclosure of Funding The work was supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China [Project No.: CUHK 14205618], Tencent AI Lab RhinoBird Focused Research Program GF202005, and NSFC Grant No U1936205
Study subjects and analysis
graphs and their samples: 4
As shown in Figure 3, this training dynamics can significantly relieve the posterior collapse problem. Visualization We plot four graphs and their samples generated by DGVAE in Figure 2 with latent cluster dimension K = 3. We let the number of edges for graph samples equal with the number for the input graphs

benchmark data sets: 3
6.2 Graph clustering. Data and baselines We use three benchmark data sets, i.e., Pubmed, Citeseer [24] and Wiki [34]. Statistics of the data sets can be found in Table 3 in Appendix

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  • 0. Hence, the regularization is used to maximize the sample variance. Assume that only samples are accessible to the target distribution Dir(β), we consider the variance instead (i.e., ECi∼Dir(β)[
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Author
Jia Li
Jia Li
Jianwei Yu
Jianwei Yu
Jiajin Li
Jiajin Li
Honglei Zhang
Honglei Zhang
Kangfei Zhao
Kangfei Zhao
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