Graph representation learning is of paramount importance for a variety graph analytical tasks, ranging from node classification to community detection. Recently, convolutional networks (GCNs) have been successfully applied learning. These GCNs generate by aggregating features the neighborhoods, which follows “neighborhood aggregation” scheme. In spite having achieved promising performance on va...

A covering projection from a graph G onto a graph H is a \local isomorphism": a mapping from the vertex set of G onto the vertex set of H such that, for every v 2 V (G), the neighborhood of v is mapped bijectively onto the neighborhood (in H) of the image of v. We investigate two concepts that concern graph covers of regular graphs. The rst one is called \multicovers": we show that for any regu...

In Bayesian disease mapping, one needs to specify a neighborhood structure to make inference on the underlying geographical relative risks. We propose a model in which the neighborhood structure is part of the parameter space. We retain the Markov property of the usual Bayesian spatial models: given the neighborhood graph, the disease rates follow a conditional autoregressive model. However, th...

Most nonlinear dimensionality reduction approaches such as Isomap heavily depend on the neighborhood structure of manifold. They determine the neighborhood graph using Euclidean distance so that they often fail to nicely deal with sparsely sampled or noise contaminated data. This paper applies the graph algebra to optimize the neighborhood structure for Isomap. The improved Isomap outperforms t...

Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears all views, it is common real-world applications for instances to be missing from some resulting incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) learning, which aims use multiple views as fully possible flexi...