This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

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

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

In the fast-growing scholarly big data background, social network technologies have recently
aroused widespread attention in academia and industry. The concept of academic social …

Community detection algorithms are fundamental tools that allow us to uncover
organizational principles in networks. When detecting communities, there are two possible …

One of the greatest advancements of the modern era is the evolution of smart grid (SG),
which integrates information communication technologies with advanced power electronic …

Graph clustering and graph outlier detection have been studied extensively on plain graphs,
with various applications. Recently, algorithms have been extended to graphs with attributes …

The key point of anomaly detection on attributed networks lies in the seamless integration of
network structure information and attribute information. A vast majority of existing works are …

The diffusion method is one of the main methods of community detection in complex
networks. In this method, the use of the concept that diffusion within the nodes that are …

The explosion of modeling complex systems using attributed networks boosts the research
on anomaly detection in such networks, which can be applied in various high-impact …