A survey of community detection in complex networks using nonnegative matrix factorization
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 …
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …
Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding
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 …
by the following three deficiencies. First, the requirement to post-processing, such as K …
Community detection in attributed graphs: An embedding approach
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 …
connected groups of nodes and well separates them from others in graphs. With the …
Scalable temporal latent space inference for link prediction in dynamic social networks
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 …
where the goal is to predict links over time based on a sequence of previous graph …
Approximate closest community search in networks
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 …
social and information networks: given one or more query nodes, find densely connected …
Unsupervised metric fusion over multiview data by graph random walk-based cross-view diffusion
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 …
representations may combat this problem from different aspects; as visual data objects …
SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering
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 …
product of two nonnegative factors. NMF has been shown to produce clustering results that …
Unsupervised feature selection via nonnegative spectral analysis and redundancy control
Z Li, J Tang - IEEE Transactions on Image Processing, 2015 - ieeexplore.ieee.org
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 …
currently described by high-dimensional features, which are often redundant and noisy …
Deep self-evolution clustering
Clustering is a crucial but challenging task in pattern analysis and machine learning.
Existing methods often ignore the combination between representation learning and …
Existing methods often ignore the combination between representation learning and …
Link prediction in temporal networks: Integrating survival analysis and game theory
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 …
real-world applications such as recommendation systems, information retrieval, and …