Stacked Graph Filter

• URL: http://arxiv.org/abs/2011.10988v1
• Date: Sun, 22 Nov 2020 11:20:14 GMT
• Title: Stacked Graph Filter
• Authors: Hoang NT and Takanori Maehara and Tsuyoshi Murata
• Abstract summary: We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint. We find that by stacking graph filters with learnable solution parameters, we can build a highly adaptive and robust graph classification model.
• Score: 19.343260981528186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully connected weights versus trainable polynomial coefficients. We find that by stacking graph filters with learnable polynomial parameters, we can build a highly adaptive and robust vertex classification model. Our treatment here relaxes the low-frequency (or equivalently, high homophily) assumptions in existing vertex classification models, resulting a more ubiquitous solution in terms of spectral properties. Empirically, by using only one hyper-parameter setting, our model achieves strong results on most benchmark datasets across the frequency spectrum.

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