Complex networks consist of a useful area for the development of research in the area of ​​finance and economics. In economics and finance the focus is on the decision-making process for each agent and its consequences. These decisions can be analyzed from the point of view of networks, since they show interconnections between the decision-making processes of different individuals. The use of complex networks makes it possible to analyze which shares of the exchange are more interconnected and could suffer contagion in the event of adverse shocks in any of them. The structure of the complex network allows to assess which ones can generate contagion and which are more vulnerable to this type of shocks.

Several relevant economic actors are interconnected and analyzing how these interconnections develop and evolve over time is crucial to assess potential risks to economic systems. For example, banks can lend resources to the same agents, creating a network of interconnections between banks that lend to the same borrowers. The same bank can borrow from several agents who now have an interconnection with each other - if the bank goes into default, everyone can be affected. If there are still more connections between these agents, adverse shocks can lead to a cascading default process. Some relevant economic and financial questions can be answered using complex network theory, such as what are the most relevant actors in an economic or financial system, which actors can bring a system to bankruptcy, and which actors are most vulnerable?

The aim of this Special Issue is to collate research articles with a focus on answering questions within this topic. We also welcome review articles that discuss the current state of the art.

Potential topics include but are not limited to the following:

• Economic, interdependent and/or social complex networks
• Complex networks, topology, risk and contagion
• Endogenous financial networks
• Investor complex networks
• Collective behavior and complex network
• Bayesian dynamic financial networks
• Multiplex complex networks and link prediction
• Machine learning and econometrics of complex networks
• Agent based modeling, chaos, genetic algorithms with complex network data
• Cellular automata, neural networks, deep learning and complex networks
• Mean field theory and evolutionary game theory with complex networks