Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …

The application of most existing multi-view spectral clustering methods is generally limited
by the following three deficiencies. First, the requirement to post-processing, such as K …

Community detection is a fundamental and widely-studied problem that finds all densely-
connected groups of nodes and well separates them from others in graphs. With the …

We propose a temporal latent space model for link prediction in dynamic social networks,
where the goal is to predict links over time based on a sequence of previous graph …

Recently, there has been significant interest in the study of the community search problem in
social and information networks: given one or more query nodes, find densely connected …

Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature
representations may combat this problem from different aspects; as visual data objects …

Nonnegative matrix factorization (NMF) provides a lower rank approximation of a matrix by a
product of two nonnegative factors. NMF has been shown to produce clustering results that …

In many image processing and pattern recognition problems, visual contents of images are
currently described by high-dimensional features, which are often redundant and noisy …

Clustering is a crucial but challenging task in pattern analysis and machine learning.
Existing methods often ignore the combination between representation learning and …

Link prediction is an important task in complex network analysis and can be found in many
real-world applications such as recommendation systems, information retrieval, and …