text: 468,58,1085133191936200709,3228486315,Daniele Grattarola,"On Arxiv, new paper in collab with @UiTromso! We introduce a new type of spectral convolution on graphs, based on ARMA filters (better response w.r.t. poly ones). Great results on node/graph signals/whole graphs classification. These things rock! <LINK> <LINK> @UiTromso We also formulate and explore the application of a principled pooling strategy for graphs, improving over previous solutions. The combo of the two results in a slim model and fast training times. <LINK> @UiTromso Results on node classification (citation nets), graph signal classification (20 news), and graph classification (graph kernel database). <LINK> Code will be released as #Keras layers + utils + experiments, as soon the paper is published. Will also be integrated as part of Spektral, our (yet to be released) framework for GNNs. HUGE shoutout to @Slackericida for coming up with the idea, it was incredibly fun to work together on this paper! @Slackericida CC @m_deff @thomaskipf @PetarV_93 @PeterWBattaglia @KleineBottleM @mys_007 all cited in the paper",http://arxiv.org/abs/1901.01343,"Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better captures the global graph structure. We propose a graph neural network implementation of the ARMA filter with a recursive and distributed formulation, obtaining a convolutional layer that is efficient to train, localized in the node space, and can be transferred to new graphs at test time. We perform a spectral analysis to study the filtering effect of the proposed ARMA layer and report experiments on four downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression. Results show that the proposed ARMA layer brings significant improvements over graph neural networks based on polynomial filters. ",Graph Neural Networks with convolutional ARMA filters,6,"['On Arxiv, new paper in collab with @UiTromso!\nWe introduce a new type of spectral convolution on graphs, based on ARMA filters (better response w.r.t. poly ones). \nGreat results on node/graph signals/whole graphs classification. These things rock!\n\n<LINK> <LINK>', '@UiTromso We also formulate and explore the application of a principled pooling strategy for graphs, improving over previous solutions. The combo of the two results in a slim model and fast training times. https://t.co/5l4Nl1DLwF', '@UiTromso Results on node classification (citation nets), graph signal classification (20 news), and graph classification (graph kernel database). https://t.co/47MplxHoW7', 'Code will be released as #Keras layers + utils + experiments, as soon the paper is published. \nWill also be integrated as part of Spektral, our (yet to be released) framework for GNNs.', 'HUGE shoutout to @Slackericida for coming up with the idea, it was incredibly fun to work together on this paper!', '@Slackericida CC @m_deff @thomaskipf @PetarV_93 @PeterWBattaglia @KleineBottleM @mys_007 \nall cited in the paper']",19,01,1037