Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

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 …

Community detection in social networks is the process of identifying the cohesive groups of
similar nodes. Detection of these groups can be helpful in many applications, such as …

Community detection is of great help to understand the structures and functions of complex
networks. It has become one of popular research topics in the field of complex networks …

Community detection is an important and challenging task in complex attribute network
analysis. Symmetric non-negative matrix factorization-based methods have become …

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 …

Community discovery can discover the community structure in a network, and it provides
consumers with personalized services and information pushing. It plays an important role in …

Network embedding, aiming to learn low-dimensional representations of nodes in networks,
is very useful for many vector-based machine learning algorithms and has become a hot …

The use of wireless sensor networks has significantly increased in the last decade. These
networks consist of a large number of small sensors, which are efficient tools for data …

This research aims to tackle the talent development problem in universities by creating a
smart employment recommendation system for recent college graduates. By combining an …