The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …

To identify influential nodes in real networks, it is essential to note the importance of
considering the local and global information in a network. In addition, it is also key to …

The identification of important nodes in complex networks is an area of exciting growth due
to its applications across various disciplines like disease control, data mining and network …

Using cross-asset return data in global financial markets, we propose a novel empirical
framework to identify the causal structure of the asset risk spillover network. The joint return …

A revolution in computational methods and statistics to process and analyse data into insight
and knowledge is along with the growth of data. The paradigm of data analytic is changed …

With the development of data driven decision making and prediction, time-series data are
ubiquitous and the demand for its classification is vast. Although a large body of research …

Directed acyclic graphs (DAGs) are powerful tools for detecting causality among variables
and thus, they have attracted increasing interest in recent years. In most previous studies …

In the context of data imbalance probably the most investigated problem is imbalance of
classes, as learning from the data with this characteristic makes detection of existing …

The paper presents research dedicated to observations of relations between attribute
properties and discretisation. In the investigations described, the gradually increasing sets of …

With the development of complex network theory, many phenomena on complex networks,
such as infectious disease transmission, information spreading and transportation …