A Simulation Tool for Exploring the Evolution of Temporal Interbank Networks

Guleva, Valentina Y.; Bochenina, Klavdiya; Skvorcova, Maria V.; Boukhanovsky, Alexander V.

doi:10.18564/jasss.3544

Cited by 4 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other studies have explored spreading dynamics in the temporal network, such as information spreading [41,42], knowledge diffusion [43,44], and disease transmission [45,46]. In addition, some studies have applied temporal network analysis to various fields in real-life settings, specifically analyzing temporal networks in financial markets [47,48], the fiscal domain [49], air traffic [50], patent opposition and collaboration [33], etc. It can be seen that temporal networks have received extensive attention and research, providing a more applicable analysis tool for studying most real networks with time attributes.…”

Section: Introductionmentioning

confidence: 99%

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Liu

Chen

et al. 2023

Systems

View full text Add to dashboard Cite

With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.

show abstract

Section: Introductionmentioning

confidence: 99%

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Liu

Chen

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…The agent-based modeling literature has focused on replicating some of these characteristics as in the work of Gurgone et al (2018) and Liu et al (2018) and within dynamic modeling frameworks, as in Zhang et al (2018), Guleva et al (2017), Xu et al (2016), andCapponi andChen (2015). Lux (2015) introduces a simple dynamic agentbased model that, starting from a heterogeneous bank size distribution and relying on a reinforcement learning algorithm based on trust, allows the system to naturally evolve toward a core-periphery structure where core banks assume the role of mediators between the liquidity needs of many smaller banks.…”

Section: Introductionmentioning

confidence: 99%

Constructing banking networks under decreasing costs of link formation

2021

View full text Add to dashboard Cite

The structure of networks plays a central role in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: banks’ assets and liabilities are typically known, but not who is lending how much and to whom. This paper adds to the existing literature in two ways. First, it shows how to simulate realistic networks that are based on balance-sheet information. To do so, we introduce a model where links cause fixed-costs, independent of contract size; but the costs per link decrease the more connected a bank is (scale economies). Second, to approach the optimization problem, we develop a new algorithm inspired by the transportation planning literature and research in stochastic search heuristics. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure.

show abstract

“…ese empirical investigations establish a few stylized facts of interbank lending, such as a typical core-periphery structure, network sparsity, and disassortativeness. e agent-based modeling literature has focused on replicating some of these characteristics, as in the work of Gurgone et al (2018) and Liu et al (2018) and within dynamic modeling frameworks, as in Zhang et al (2018), Guleva et al (2017), Xu et al (2016), and Capponi and Chen (2015). Lux (2015) introduces a simple dynamic agent-based model that, starting from a heterogeneous bank size distribution and relying on a reinforcement learning algorithm based on trust, allows the system to naturally evolve toward a core-periphery structure where core banks assume the role of mediators between the liquidity needs of many smaller banks.…”

Section: Introductionmentioning

confidence: 99%

Re-Creating Banking Networks under Decreasing Fixed Costs

Maringer

Craig

Paterlini

2019

Working Paper (Federal Reserve Bank of Cleveland)

View full text Add to dashboard Cite

Theory emphasizes the central role of the structure of networks in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: Banks' assets and liabilities are typically known, but not who is lending how much and to whom. We first show how to simulate realistic networks that are based on balance-sheet information by minimizing costs where there is a fixed cost to forming a link. Second, we also show how to do this for a model with fixed costs that are decreasing in the number of links. To approach the optimization problem, we develop a new algorithm based on the transportation planning literature. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure.

show abstract

A Simulation Tool for Exploring the Evolution of Temporal Interbank Networks

Cited by 4 publications

References 0 publications

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective

Constructing banking networks under decreasing costs of link formation

Re-Creating Banking Networks under Decreasing Fixed Costs

Contact Info

Product

Resources

About