Detecting botnets in a network is crucial because bots impact numerous areas such as cyber
security, finance, health care, law enforcement, and more. Botnets are becoming more …

In an era where accumulating data is easy and storing it inexpensive, feature selection plays
a central role in helping to reduce the high-dimensionality of huge amounts of otherwise …

Optimum-path forest (OPF) is a graph-based machine learning method that can overcome
some limitations of the traditional machine learning algorithms that have been used in …

Social network analysis (SNA) is a well-established methodology for investigating networks
through the use of mathematical formulations abstracted from graph theory. It has been …

The emergence of multifarious complex networks has attracted researchers and
practitioners from various disciplines. Discovering cohesive subgroups or communities in …

Measuring centrality in a social network, especially in bipartite mode, poses many
challenges, for example, the requirement of full knowledge of the network topology, and the …

We introduce H-group closeness centrality in this work. H-group closeness centrality of a
group of nodes measures how close this node group is to other nodes in a graph, and can …

Graph computing is widely applied in a large number of big data applications. Despite its
importance, high performance graph computing remains a challenge, especially for large …

Technological advances especially in the miniaturization of robotic devices foreshadow the
emergence of large-scale ensembles of small-size resource-constrained robots that …

This book deals with the dynamics of international specializations during the present period
of trade globalization. It discusses international trade as a network linking countries, and …