In the past few years, semi-supervised node classification in attributed network has been
developed rapidly. Inspired by the success of deep learning, researchers adopt the …

Conventional semi-supervised clustering approaches have several shortcomings, such as
(1) not fully utilizing all useful must-link and cannot-link constraints,(2) not considering how …

In this paper, we present an interactive approach for shape co-segmentation via label
propagation. Our intuitive approach is able to produce error-free results and is very effective …

In this article, we propose a novel model for constrained clustering, namely, the dissimilarity
propagation-guided graph-Laplacian principal component analysis (DP-GLPCA). By fully …

Constrained spectral clustering (SC) based on pairwise constraint propagation has attracted
much attention due to the good performance. All the existing methods could be generally …

Recently community detection has attracted much interest in social media to understand the
collective behaviours of users and allow individuals to be modeled in the context of the …

Mainstream semi-supervised classification assumes that part of the available data are
labelled. Here we assume that, in addition to the labels, we have pairwise constraints on the …

Abstract G raph C onvolutional N etwork (GCN) has been commonly applied for semi-
supervised learning tasks. However, the established GCN frequently only considers the …

While clustering is usually an unsupervised operation, there are circumstances where we
have access to prior belief that pairs of samples should (or should not) be assigned with the …

In most real world clustering scenarios, experts generally dispose of limited background
information, but such knowledge is valuable and may guide the analysis process. Semi …