Value of peripheral nodes in controlling multilayer scale-free networks

Zhang, Yan; Garas, Antonios; Schweitzer, Frank

doi:10.1103/physreve.93.012309

Cited by 21 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A much smarter solution, however, would focus only on a few agents, namely those with the ability to prevent large cascades. The problem to identify those agents is addressed in research about network controllability [6,14], which is related to control theory. The method assigns a control signal, i.e., an incentive to stay or to leave, to the identified agents with the most influence on the network dynamics [15], which are called driver nodes.…”

Section: Network Interventionsmentioning

confidence: 99%

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions

Casiraghi

Schweitzer

2020

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. OSN are very volatile because users enter and exit and frequently change their interactions. This makes the robustness of such systems difficult to measure and to control. To quantify robustness, we propose a coreness value obtained from the directed interaction network. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. For agents, we define a utility function that depends on their relative reputation and their costs for interactions. The decision of agents to leave the OSN depends on this utility. Our aim is to prevent dropout cascades by influencing specific agents with low utility. We identify strategies to control agents in the core and the periphery of the OSN such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased.

show abstract

Section: Network Interventionsmentioning

confidence: 99%

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions

Casiraghi

Schweitzer

2020

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Early attempts have focused on the issues of controllability or observability with linear dynamics (Chapman et al, 2014;Menichetti et al, 2015;Wang et al, 2015;Yuan et al, 2014;Zhang et al, 2016;Zhou, 2015). For example, some controllability conditions on the overall network topology, the node dynamics, the external control inputs and the inner interactions have been derived for a networked MIMO system .…”

Section: Controlling Network Of Networkmentioning

confidence: 99%

Control principles of complex systems

Liu¹,

Barabási²

2016

Rev. Mod. Phys.

536

349

View full text Add to dashboard Cite

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of complex networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

show abstract

“…Apart from temporal information, we often have access to data that capture multiple types of relations or interactions. The resulting multi-layer network topologies give rise to complications that threaten standard techniques, e.g., to infer and analyze social networks, detect community structures, or to model and control dynamical processes in networked systems [28,16,7,35,3].…”

Section: Motivationmentioning

confidence: 99%

From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles

Casiraghi

Nanumyan

Scholtes

et al. 2017

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.

show abstract

Value of peripheral nodes in controlling multilayer scale-free networks

Cited by 21 publications

References 27 publications

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions

Improving the Robustness of Online Social Networks: A Simulation Approach of Network Interventions

Control principles of complex systems

From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles

Contact Info

Product

Resources

About