… approach to learning with graphstructured data in the problem domain of graph classification…
readout operation to aggregate node features into a graph-level representation. To this end, …

… To overcome these issues, we propose BernNet, a novel graph … and learning arbitrary graph
spectral filters. In particular, for any filter over the normalized Laplacian spectrum of a graph, …

… For example, in addition to the example in [10], the recent work in [11] has proposed to learn
a graph filter that leads to the most suitable GP kernel for the observed graph data, hence …

… /item graph shown in Figure 1(a). To remove the graph noise, we devise a 2D graph filter
called LCF … Similar to the filter, we also extend 2D convolution from the Euclidean domain to the …

… signal x with an FIR graph filter H(S); thus, we refer to the … We can learn the filter taps by
solving (7) with the GCNN map ( ; , ). x S H U To do so, we use some optimization method based …

… average (ARMA) filters and context-aware learning (DARMA-CAL) is proposed for … filter
instead of a spectral filter to apply to GNNs. The ARMA filter can better capture the global graph …

… to combine the different graph filters, with which the graph filter can be adaptively determined
… utilized to learn the deep global and contextual information of the graph. Simultaneously, …

… We propose a graph neural network implementation of the ARMA filter with a recursive and
… Here, we consider a machine learning approach that does not require to specify the target …

… The aim of the paper was to solve the problem of joint identification of a graph filter and its …
be viewed as a specific bandstop graph filter on networks representing correlations between …

… With the overall filter network structure to filter original embeddings in a filter space, we argue
that the fairness-aware recommender systems need to satisfy two goals: representative for …