This paper extensively reviews the literature of community detection in complex networks
and proposes a general classification describing the main models used for this purpose …

Clustering is a fundamental research field and plays an important role in data analysis. To
better address the relationship between an element and a cluster, a Three-Way clustering …

Community detection is an important step in perceiving network structure and performance
for complex network analysis. The rapid growth of network data in recent years has piqued …

The core of most existing graph-based multi-view clustering methods is to learn a rigid
consistent spectral embedding from multiple graphs. In practice, however, such a …

Multi-view clustering has been a hotspot in the field of machine learning and pattern
recognition, and methods based on non-negative matrix factorization have gained attention …

Abstract Recently, Incomplete Multi-View Clustering (IMVC) has become a rapidly growing
research topic, driven by the prevalent issue of incomplete data in real-world applications …

Community detection in social networks is one of the advertising methods in electronic
marketing. One of the approaches to find communities in large social networks is to use …

Networks in the real world are dynamic and evolving. The most critical process in networks
is to determine the structure of the community, based on which we can detect hidden …

The target of the clustering analysis is to group a set of data points into several clusters
based on the similarity or distance. The similarity or distance is usually a scalar used in …

Abstract Nonnegative Matrix Factorization (NMF) has been widely used to resolve the
problem of community detection in complex networks. The present NMF-based methods for …