Abstract
Graph neural networks (GNNs) have found majority of applications in multiple domains including physical science, molecular biology, etc. However, there is still scope of research in the application of GNNs in electrical domain. This research work tries to generate the circuit graphs that analyzes the GNNs. The end goal is to create a mechanism to iteratively predict the graph structure and thus complete a incomplete circuit diagram. The research work firstly tries to find out how well GNN can be used for predicting the missing node label or the missing node geometric features in the subset of the graph. Then, application of GNNs to anomaly detection problem is investigated. Next, GNN architecture is used to accurately predict the node label and approximate geometric features of the missing node in the circuit graph. Furthermore, a Graph Autoencoder (GAE) model is created and used for pruning the wrong edges in the circuit graph.
The GNN model created for the purpose of anomaly detection problem gave around 90% accuracy. The GNN model used for missing node feature estimation gave around 89% accuracy to predict the correct label for missing node and it also performed effectively in approximating the geometric features of the missing node correctly. The link prediction model is able to classify the correct edges 92% of the times. Finally, a mechanism is provided that iteratively predicts graph structure using the anomaly detection model, node feature prediction model and link prediction model in a cycle.
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References
Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. arXiv preprint arXiv:1903.02428 (2019)
Hagberg, A., Swart, P., Chult, D.S.: Exploring network structure, dynamics, and function using networkX. Technical report, Los Alamos National Lab. (LANL), Los Alamos, NM (United States) (2008)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
He, M., Wei, Z., Wen, J.R.: Convolutional neural networks on graphs with Chebyshev approximation, revisited. arXiv preprint arXiv:2202.03580 (2022)
Lu, Y.C., Pentapati, S., Lim, S.K.: VLSI placement optimization using graph neural networks. In: Proceedings of the 34th Advances in Neural Information Processing Systems (NeurIPS) Workshop on ML for Systems, Virtual, pp. 6–12 (2020)
Mirhoseini, A., et al.: A graph placement methodology for fast chip design. Nature 594(7862), 207–212 (2021)
Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 (2018)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009). https://doi.org/10.1109/TNN.2008.2005605
Thoma, F., Bayer, J., Li, Y.: A public ground-truth dataset for handwritten circuit diagram images. CoRR abs/2107.10373 (2021). https://arxiv.org/abs/2107.10373
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Patil, J.M., Bayer, J., Dengel, A. (2023). Graph Neural Networks for Circuit Diagram Pattern Generation. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_24
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DOI: https://doi.org/10.1007/978-3-031-39059-3_24
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