Contrastive Multi-View Representation Learning on Graphs

ICML 2020, 2020.

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We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs

Abstract:

We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting multi-scale encodings do not improve performance, and the best performance is achieved by co...More

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Introduction
  • Graph neural networks (GNN) (Li et al, 2015; Gilmer et al, 2017; Kipf & Welling, 2017; Velickovicet al., 2018; Xu et al, 2019b) reconcile the expressive power of graphs in modeling interactions with unparalleled capacity of deep models in learning representations.
  • Recent works on contrastive learning by maximizing mutual information (MI) between node and graph representations have achieved state-of-the-art results on both node classification (Velickovicet al., 2019) and graph classification (Sun et al, 2020) tasks.
  • These methods require specialized encoders to learn graph or node level representations
Highlights
  • Graph neural networks (GNN) (Li et al, 2015; Gilmer et al, 2017; Kipf & Welling, 2017; Velickovicet al., 2018; Xu et al, 2019b) reconcile the expressive power of graphs in modeling interactions with unparalleled capacity of deep models in learning representations
  • To further improve contrastive representation learning on node and graph classification tasks, we systematically study the major components of our framework and surprisingly show that unlike visual contrastive learning: (1) increasing the number of views, i.e., augmentations, to more than two views does not improve the performance and the best performance is achieved by contrasting encodings from first-order neighbors and a general graph diffusion, (2) contrasting node and graph encodings across views achieves better results on both tasks compared to contrasting graphgraph or multi-scale encodings, (3) a simple graph readout layer achieves better performance on both tasks compared to hierarchical graph pooling methods such as differentiable pooling (DiffPool) (Ying et al, 2018), and (4) applying regularization or normalization layers has a negative effect on the performance
  • We use three node classification and five graph classification benchmarks widely used in the literature (Kipf & Welling, 2017; Velickovicet al., 2018; 2019; Sun et al, 2020)
  • We introduced a self-supervised approach for learning node and graph level representations by contrasting encodings from two structural views of graphs including first-order neighbors and a graph diffusion
  • We showed that unlike visual representation learning, increasing the number of views or contrasting multi-scale encodings do not improve the performance
  • On Cora, we achieve 86.8% accuracy, which is a 5.5% relative improvement over previous state-of-the-art
  • We achieved new state-of-the-art in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol and outperformed strong supervised baselines in 4 out of 8 benchmarks
Methods
  • Inspired by recent advances in multi-view contrastive learning for visual representation learning, the approach learns node and graph representations by maximizing MI between node representations of one view and graph representation of another view and vice versa which achieves better results compared to contrasting global or multi-scale encodings on both node and graph classification tasks.
  • The authors can consider two types of augmentations on graphs: (1) feature-space augmentations operating on initial node features, e.g., masking or adding Gaussian noise, and (2) structure-space augmentations and corruptions operating on graph structure by adding or removing connectivities, sub-sampling, or generating global views using shortest distances or diffusion matrices.
  • MLP (VELIC KOVIC ET AL., 2018) ICA (LU & GETOOR, 2003) LP (ZHU ET AL., 2003) MANIREG (BELKIN ET AL., 2006) SEMIEMB (WESTON ET AL., 2012) PLANETOID (YANG ET AL., 2016) CHEBYSHEV (DEFFERRARD ET AL., 2016) GCN (KIPF & WELLING, 2017) MONET (MONTI ET AL., 2017) JKNET (XU ET AL., 2018) GAT (VELIC KOVIC ET AL., 2018)
Results
Conclusion
  • The authors introduced a self-supervised approach for learning node and graph level representations by contrasting encodings from two structural views of graphs including first-order neighbors and a graph diffusion.
  • The authors showed that unlike visual representation learning, increasing the number of views or contrasting multi-scale encodings do not improve the performance.
  • Using these findings, the authors achieved new state-of-the-art in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol and outperformed strong supervised baselines in 4 out of 8 benchmarks.
  • The authors are planning to investigate large pre-training and transfer learning capabilities of the proposed method
Summary
  • Introduction:

    Graph neural networks (GNN) (Li et al, 2015; Gilmer et al, 2017; Kipf & Welling, 2017; Velickovicet al., 2018; Xu et al, 2019b) reconcile the expressive power of graphs in modeling interactions with unparalleled capacity of deep models in learning representations.
  • Recent works on contrastive learning by maximizing mutual information (MI) between node and graph representations have achieved state-of-the-art results on both node classification (Velickovicet al., 2019) and graph classification (Sun et al, 2020) tasks.
  • These methods require specialized encoders to learn graph or node level representations
  • Methods:

    Inspired by recent advances in multi-view contrastive learning for visual representation learning, the approach learns node and graph representations by maximizing MI between node representations of one view and graph representation of another view and vice versa which achieves better results compared to contrasting global or multi-scale encodings on both node and graph classification tasks.
  • The authors can consider two types of augmentations on graphs: (1) feature-space augmentations operating on initial node features, e.g., masking or adding Gaussian noise, and (2) structure-space augmentations and corruptions operating on graph structure by adding or removing connectivities, sub-sampling, or generating global views using shortest distances or diffusion matrices.
  • MLP (VELIC KOVIC ET AL., 2018) ICA (LU & GETOOR, 2003) LP (ZHU ET AL., 2003) MANIREG (BELKIN ET AL., 2006) SEMIEMB (WESTON ET AL., 2012) PLANETOID (YANG ET AL., 2016) CHEBYSHEV (DEFFERRARD ET AL., 2016) GCN (KIPF & WELLING, 2017) MONET (MONTI ET AL., 2017) JKNET (XU ET AL., 2018) GAT (VELIC KOVIC ET AL., 2018)
  • Results:

    The authors use Citeseer, Cora, and Pubmed citation networks (Sen et al, 2008) where documents are connected through citations.
  • The authors use the following: MUTAG (Kriege & Mutzel, 2012) containing mutagenic compounds, PTC (Kriege & Mutzel, 2012) containing compounds tested for carcinogenicity, Reddit-Binary (Yanardag & Vishwana, 2015) connecting users through responses in Reddit online discussions, and IMDB-Binary and IMDB-Multi (Yanardag & Vishwana, 2015) connecting actors/actresses based on movie appearances.
  • Conclusion:

    The authors introduced a self-supervised approach for learning node and graph level representations by contrasting encodings from two structural views of graphs including first-order neighbors and a graph diffusion.
  • The authors showed that unlike visual representation learning, increasing the number of views or contrasting multi-scale encodings do not improve the performance.
  • Using these findings, the authors achieved new state-of-the-art in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol and outperformed strong supervised baselines in 4 out of 8 benchmarks.
  • The authors are planning to investigate large pre-training and transfer learning capabilities of the proposed method
Tables
  • Table1: Statistics of classification benchmarks
  • Table2: Mean node classification accuracy for supervised and unsupervised models. The input column highlights the data available to each model during training (X: features, A: adjacency matrix, S: diffusion matrix, Y: labels)
  • Table3: Performance on node clustering task reported in normalized MI (NMI) and adjusted rand index (ARI) measures
  • Table4: Mean 10-fold cross validation accuracy on graphs for kernel, supervised, and unsupervised methods
  • Table5: Effect of MI estimator, contrastive mode, and views on the accuracy on both node and graph classification tasks
Download tables as Excel
Related work
Funding
  • We achieve new state-ofthe-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol
  • On Cora (node) and Reddit-Binary (graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which are 5.5% and 2.4% relative improvements over previous state-of-the-art
  • To further improve contrastive representation learning on node and graph classification tasks, we systematically study the major components of our framework and surprisingly show that unlike visual contrastive learning: (1) increasing the number of views, i.e., augmentations, to more than two views does not improve the performance and the best performance is achieved by contrasting encodings from first-order neighbors and a general graph diffusion, (2) contrasting node and graph encodings across views achieves better results on both tasks compared to contrasting graphgraph or multi-scale encodings, (3) a simple graph readout layer achieves better performance on both tasks compared to hierarchical graph pooling methods such as differentiable pooling (DiffPool) (Ying et al, 2018), and (4) applying regularization (except early-stopping) or normalization layers has a negative effect on the performance. Using these findings, we achieve new state-of-the-art in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol
  • On Cora node classification benchmark, our approach achieves 86.8% accuracy, which is a 5.5% relative improvement over previous state-of-the-art (Velickovicet al., 2019), and on Reddit-Binary graph classification benchmark, it achieves 84.5% accuracy, i.e., a 2.4% relative improvement over previous state-of-the-art (Sun et al, 2020)
  • The results reported in Table 2 show that we achieve state-of-the-art results with respect to previous unsupervised models
  • On Cora, we achieve 86.8% accuracy, which is a 5.5% relative improvement over previous state-of-the-art
  • The results shown in Table 3 suggest that our model achieves state-of-the-art NMI and ARI scores across all benchmarks
  • The results shown in Table 4 suggest that our approach achieves state-of-the-art results with respect to unsupervised models
  • On Reddit-Binary (Yanardag & Vishwana, 2015), it achieves 84.5% accuracy, i.e., a 2.4% relative improvement over previous state-of-the-art
  • It is noteworthy that we achieve the state-of-the-art results on both node and graph classification benchmarks using a unified approach and unlike previous unsupervised models (Velickovicet al., 2019; Sun et al, 2020), we do not devise a specialized encoder for each task
  • Furthermore, we investigated whether increasing the number of views increases the performance on down-stream tasks, monotonically
  • We observed that applying the former achieves significantly better results compared to the latter or a combination of both
  • We showed that unlike visual representation learning, increasing the number of views or contrasting multi-scale encodings do not improve the performance. Using these findings, we achieved new state-of-the-art in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol and outperformed strong supervised baselines in 4 out of 8 benchmarks
Study subjects and analysis
datasets: 5
For example, on Reddit-Binary (Yanardag & Vishwana, 2015), it achieves 84.5% accuracy, i.e., a 2.4% relative improvement over previous state-of-the-art. Our model also outperforms kernel methods in 4 out of 5 datasets and also outperforms best supervised model in one of the datasets. When compared to supervised baselines individually, our model outperforms GCN and GAT models in 3 out of 5 datasets, e.g., a 5.3% relative improvement over GAT on IMDB-Binary dataset

datasets: 5
Our model also outperforms kernel methods in 4 out of 5 datasets and also outperforms best supervised model in one of the datasets. When compared to supervised baselines individually, our model outperforms GCN and GAT models in 3 out of 5 datasets, e.g., a 5.3% relative improvement over GAT on IMDB-Binary dataset. It is noteworthy that we achieve the state-of-the-art results on both node and graph classification benchmarks using a unified approach and unlike previous unsupervised models (Velickovicet al., 2019; Sun et al, 2020), we do not devise a specialized encoder for each task

datasets: 3
We investigated four MI estimators including: noisecontrastive estimation (NCE) (Gutmann & Hyvarinen, 2010; Oord et al, 2018), Jensen-Shannon (JSD) estimator following formulation in (Nowozin et al, 2016), normalized temperature-scaled cross-entropy (NT-Xent) (Chen et al, 2020), and Donsker-Varadhan (DV) representation of the KL-divergence (Donsker & Varadhan, 1975). The results shown in Table 5 suggests that Jensen-Shannon estimator achieves better results across all graph classification benchmarks, whereas in node classification benchmarks, NCE achieves better results in 2 out of 3 datasets. 4.4.2

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