Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in representation learning. However, existing SGNNs are limited implementing filters with rigid transforms and cannot adapt to signals residing on graphs tasks at hand. In this paper, we propose a novel class of that realizes adaptive wavelets. Specifically, the wavelets learned network-parameterized lifting ...

Graph convolutional neural networks exploit convolution operators, based on some neighborhood aggregating scheme, to compute representations of graphs. The most common operators only local topological information. To consider wider receptive fields, the mainstream approach is non-linearly stack multiple graph (GC) layers. In this way, however, interactions among GC parameters at different level...

Semi-supervised learning (SSL) provides a way to improve the performance of prediction models (e.g., classifier) via usage unlabeled samples. An effective and widely used method is construct graph that describes relationship between labeled Practical experience indicates quality significantly affects model performance. In this paper, we present visual analysis interactively constructs high-qual...

The decarbonization of the transport system requires a better understanding human mobility behavior to optimally plan and evaluate sustainable options (such as Mobility Service). Current analysis frameworks often rely on specific datasets or data-specific assumptions hence are difficult generalize other studies. In this work, we present workflow identify groups users with similar that appear ac...

Given a graph G, a visual cryptography scheme based on the graph G is a method to distribute a secret image among the vertices of G, the participants, so that a subset of participants can recover the secret image if they contain an edge of G, by stacking their shares, otherwise they can obtain no information regarding the secret image. In this paper we apply maximal independent sets of the grap...