Abstract
Despite their recent popularity, deep and efficient Graph Neural Networks remain a major challenge due to (a) over-smoothing, (b) noisy neighbours (heterophily), and (c) the suspended animation problem. Inspired by the attention mechanism’s ability to focus on selective information, and prior work on feature preserving mechanisms, we propose FDGATII, a dynamic deep-capable model that addresses all these challenges simultaneously and efficiently. Specifically, by combining Initial Residuals and Identity with the more expressive dynamic self-attention, FDGATII effectively handles noise in heterophilic graphs and is capable of depths over 32 with no over-smoothing, overcoming two main limitations of many prior GNN techniques. By using edge-lists, FDGTII avoids computationally intensive matrix operations, is parallelizable and does not require knowing the graph structure upfront. Experiments on 7 standard datasets show that FDGATII outperforms the GAT and GCN based benchmarks in accuracy and performance on fully supervised tasks. We obtain State-of-the-art (SOTA) on the highly heterophilic Chameleon and Cornell datasets with 1 layer, and come only 0.1% short of Cora SOTA with zero graph pre processing. https://github.com/gayanku/FDGATII
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References
Abu-El-Haija, S., et al.: Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing. In: International Conference on Machine Learning, pp. 21–29. PMLR (2019)
Alon, U., Yahav, E.: On the bottleneck of graph neural networks and its practical implications. In: International Conference on Learning Representations (2020)
Bright, D., Koskinen, J., Malm, A.: Illicit network dynamics: the formation and evolution of a drug trafficking network. J. Quant. Criminol. 35(2), 237–258 (2019)
Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? In: International Conference on Learning Representations (2021)
Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: International Conference on Machine Learning, pp. 1725–1735. PMLR (2020)
Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized pagerank graph neural network. In: International Conference on Learning Representations (2020)
Chitra, U., Musco, C.: Analyzing the impact of filter bubbles on social network polarization. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 115–123 (2020)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129–150 (2011)
Hardt, M., Ma, T.: Identity matters in deep learning. In: International Conference on Learning Representations (2017)
Jin, W., Derr, T., Wang, Y., Ma, Y., Liu, Z., Tang, J.: Node similarity preserving graph convolutional networks. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 148–156 (2021)
Kim, D., Oh, A.: How to find your friendly neighborhood: graph attention design with self-supervision. In: International Conference on Learning Representations (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: J. International Conference on Learning Representations (ICLR 2017) (2016)
Knyazev, B., Taylor, G.W., Amer, M.: Understanding attention and generalization in graph neural networks. Adv. Neural Inf. Process. Syst. 32, 4202–4212 (2019)
Kulatilleke, G.K., Portmann, M., Chandra, S.S.: SCGC: Self-supervised contrastive graph clustering. arXiv preprint arXiv:2204.12656 (2022)
LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)
Liu, M., Wang, Z., Ji, S.: Non-local graph neural networks. IEEE Trans. Pattern Anal. Mach. Intell. (2021)
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: EMNLP (2015)
Maurya, S.K., Liu, X., Murata, T.: Simplifying approach to node classification in graph neural networks. J. Comput. Sci. 101695 (2022)
Oono, K., Suzuki, T.: Graph neural networks exponentially lose expressive power for node classification. In: International Conference on Learning Representations (2019)
Pei, H., Wei, B., Chang, K.C.C., Lei, Y., Yang, B.: Geom-gcn: geometric graph convolutional networks. In: International Conference on Learning Representations, pp. 6519–6528 (2019)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Rong, Y., Huang, W., Xu, T., Huang, J.: Dropedge: Towards deep graph convolutional networks on node classification. In: International Conference on Learning Representations (2019)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 5998–6008 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.i., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning, pp. 5453–5462. PMLR (2018)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., Koutra, D.: Beyond homophily in graph neural networks: Current limitations and effective designs. Adv. Neural Inf. Process. Syst. (2020)
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Dedicated to Sugandi.
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Kulatilleke, G.K., Portmann, M., Ko, R., Chandra, S.S. (2022). FDGATII: Fast Dynamic Graph Attention with Initial Residual and Identity. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_6
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