… Indeed, it is not uncommon for networks to market themselves on the basis of the strengths
of their respective networks; commercials touting network superiority are not at all uncommon. …

… theory of complex networks, we focus on three main network approaches, namely, phase
space based recurrence networks, visibility graphs and Markov chain based transition networks…

… Results: Our results show that graph theory and its implications in cognitive neuroscience …
capability in characterizing the behavior of complex brain systems. Although graph theoretical …

… Finally, at the end of the chapter, I will discuss the notational scheme—one common in graph
theory—that is used throughout the book. Although for some readers the scheme might be …

… In Section IV, we propose a theoretical model to explain the results, and provide … -relevant
alternatives to the data-intensive network theory-based procedures we used in this paper, and …

… Below we provide an example of how the functional complex network approach can be
applied in different forested landscape settings. It is important to note that this approach is nested …

… Secondly, we cover different types of complex networks and their topologies, including
temporal networks, multilayer networks, and neural networks. Next, we cover network time series …

… Then, the dynamic properties of the brain network during WM were analyzed based on
graph theory. Experimental results show that the LFPs power increased at the WM state than at …

… Multi-task graph properties In the multi-task benchmark, we consider three node labels and
three graph labels based on standard graph theory problems. The node properties tasks are …

… To achieve these breakthroughs, we combine tools from Automata and Graph theory,
Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first …