Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

The most basic and significant issue in complex network analysis is community detection,
which is a branch of machine learning. Most current community detection approaches, only …

Internet of Things (IoT) is now transitioning from theory to practice. This means that a lot of
data will be generated and the management of this data is going to be a big challenge. To …

Community detection in social networks is a fundamental task of complex network analysis.
Community is usually regarded as a functional unit. Networks in real world more or less …

Community detection is an important problem in network science that discovers highly
clustered groups of nodes having similar properties. Label propagation algorithm (LPA) is …

Graph clustering techniques are very useful for detecting densely connected groups in large
graphs. Many existing graph clustering methods mainly focus on the topological structure …

Many real-world social networks such as brain graph, protein structure, food web,
transportation system, World Wide Web, online social networks exist in the form of a complex …

Abstract Internet of Things (IoT) is transitioning from theory to practice. As IoT-based services
evolve and the means of connectivity progress, a multitude of devices and objects will …

Abstract Information mining from complex networks by identifying communities is an
important problem in a number of research fields, including the social sciences, biology …

Given a graph G where each node is associated with a set of attributes, and a parameter k
specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes …