This paper provides a compressive literature review on the application of complex network
theories in the resilience evaluation and enhancement of modern power systems. First, the …

The promise of big data is enormous and nowhere is it more critical than in its potential to
contain important, undiscovered interdependence among thousands of variables. Networks …

In this paper, we aim to illustrate the concept of mutually trustworthy human-machine
knowledge automation (HM-KA) as the technical mechanism of hybrid augmented …

Network Functions Virtualization (NFV) allows softwarization of network functions and
enables to run network functions as Virtual Network Function (VNF) instances on top of the …

A methodology, to determine the causal relations between time series and to derive the set
of equations describing the interacting systems, has been developed. The techniques …

Graph convolutional networks (GCNs) and its variants are designed for unsigned graphs
containing only positive links. Many existing GCNs have been derived from the spectral …

This article attempts to look at complex graph models (specifically, the metagraph model)
through the prism of information granulation. The basic provisions and definitions of …

Revealing system behavior from observed time series is a fundamental problem worthy of in-
depth study and exploration, and has attracted extensive attention in a wide range of fields …

Network architecture design is critical for optimizing industrial networks. Network
architectures can be classified into small-scale networks and large-scale networks based on …

Association Rule Mining is a machine learning method for discovering the interesting
relations between the attributes in a huge transaction database. Typically, algorithms for …