Community detection aims at discovering the structure, behavior, dynamics, and
organization of a complex network by finding cohesive groups where nodes (entities) are, in …

Community detection in social networks has received much attention from the researchers of
multiple disciplines due to its impactful applications such as recommendation systems, link …

The community structure is a collection of individuals with common characteristics that
commonly exists in complex networks. The detection of community structures aids in mining …

Influence maximization (IM) problem, a fundamental algorithmic problem, is the problem of
selecting a set of k users (refer as seed set) from a social network to maximize the expected …

This book is written for computer engineers, scientists, and students studying/working in
reinforcement learning and artificial intelligence domains. The book collects recent …

Identifying intrusion from massive and multi-source logs accurately and in real-time presents
challenges for today's users. This article presents Paradise, a real-time, generalized, and …

Community detection is of great importance to understand the structures and functions of
networks. Overlap is a significant feature of networks and overlapping community detection …

Social networks are usually modeled and represented as deterministic graphs with a set of
nodes as users and edges as connection between users of networks. Due to the uncertain …

Due to the rapid development of internet technology, multi-attribute large-scale group
decision making (MALSGDM) becomes a research hotspot of multi-attribute group decision …

Recently, there has been growing interest in social network analysis. Graph models for
social network analysis are usually assumed to be a deterministic graph with fixed weights …