convolutional graph-tensor net for graph data completion

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Convolutional Graph - Tensor Net for Graph Data Completion IJCAI 2020 workshop on Tensor Network Representations in Machine Learning Xiao-Yang, Liu, Ming Zhu [email protected], [email protected] Presenter: Ming Zhu

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Page 1: Convolutional Graph-Tensor Net for Graph Data Completion

Convolutional Graph-Tensor Net for Graph Data Completion

IJCAI 2020 workshop on Tensor Network Representations in Machine Learning

Xiao-Yang, Liu, Ming [email protected], [email protected]

Presenter: Ming Zhu

Page 2: Convolutional Graph-Tensor Net for Graph Data Completion

Outline

• Scenario and Related Works• Graph-tensor Model • Problem Formulation• Solution• Performance Evaluation• Conclusion

Page 3: Convolutional Graph-Tensor Net for Graph Data Completion

Scenario and Related Works

• Scenario-Data with graph structures are common. -The data constitutes a graph-tensor.-How to complete a graph-tensor is an important problem.

• Related works (e.g., TNN-ADMM, imputation algorithm)-A large number of iterations.-Time-consuming.

Page 4: Convolutional Graph-Tensor Net for Graph Data Completion

Graph-tensor Model

• An undirected graph, where each node has a data matrix.

• The graph Fourier transform and the inverse graph Fourier transform are defined as

Page 5: Convolutional Graph-Tensor Net for Graph Data Completion

Problem FormulationGraph-tensor nuclear-norm:

Page 6: Convolutional Graph-Tensor Net for Graph Data Completion

Convolutional Graph-Tensor Net (Conv GT-Net)

• 1) Imputation

• 2) General Transform.F(·) = Conv2D(ReLU(Conv2D(FC(·)))).

• 3) Soft-Thresholding Operator.soft(a, λ) = max(|a| − λ, 0) · sign(a)

• 4) Inverse Transform.F−1(·) = FC(Conv2D(ReLU(Conv2D(·))))

Page 7: Convolutional Graph-Tensor Net for Graph Data Completion

Loss Function

(accuracy of the inverse function)

(fidelity of the reconstructed data)

Page 8: Convolutional Graph-Tensor Net for Graph Data Completion

Structure of Conv GT-Net

Page 9: Convolutional Graph-Tensor Net for Graph Data Completion

Comparison Algorithms

• TNN-ADMMNovel methods for multilinear data completion and de-noising based on tensor-svd. In IEEE CVPR, pages 3842–3849, 2014.

• Imputation algorithmConvolutional imputation of matrix networks. In ICML, volume 80, pages 4818–4827, 2018.

Page 10: Convolutional Graph-Tensor Net for Graph Data Completion

Experiment Settings

• Conv GT-Net is trained in TensorFlow on a server with an NVIDIA Telsa V100 GPU (16GB device memory)

• All algorithms are tested on a laptop with an i7-8750H CPU (2.20GHz) and 16GB memory.

• Data set: ego-Facebook data sets from SNAP with 4,039 nodes and 88,234 edges. We pick 100 nodes (No. 896-995) and their corresponding edges to form a graph topology.

Page 11: Convolutional Graph-Tensor Net for Graph Data Completion

Completion Accuracy vs. Missing Rate.

Page 12: Convolutional Graph-Tensor Net for Graph Data Completion

Running Time vs. Missing Rate.

Page 13: Convolutional Graph-Tensor Net for Graph Data Completion

Conclusion

• We propose Conv GT-Net to solve the graph-tensor completion problem using deep neural networks.

• Conv GT-Net provides higher completion accuracy and runs faster than the other algorithms.

Page 14: Convolutional Graph-Tensor Net for Graph Data Completion

End

Thank you

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