ArticleScientific reports2020
On the effects of memory and topology on the controllability of complex dynamical networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Target controllability: a feed-forward greedy algorithm in complex networks, meeting Kalman's rank condition.Bioinformatics (Oxford, England) · 2024Article
- The role of long-term power-law memory in controlling large-scale dynamical networks.Scientific reports · 2023Article
- The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks.Frontiers in network physiology · 2022Article
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Authors and funding
3 authors.
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Abstract
Recent advances in network science, control theory, and fractional calculus provide us with mathematical tools necessary for modeling and controlling complex dynamical networks (CDNs) that exhibit long-term memory. Selecting the minimum number of driven nodes such that the network is steered to a prescribed state is a key problem to guarantee that complex networks have a desirable behavior. Therefore, in this paper, we study the effects of long-term memory and of the topological properties on the minimum number of driven nodes and the required control energy. To this end, we introduce Gramian-based methods for optimal driven node selection for complex dynamical networks with long-term memory and by leveraging the structure of the cost function, we design a greedy algorithm to obtain near-optimal approximations in a computationally efficiently manner. We investigate how the memory and topological properties influence the control effort by considering Erdős-Rényi, Barabási-Albert and Watts-Strogatz networks whose temporal dynamics follow a fractional order state equation. We provide evidence that scale-free and small-world networks are easier to control in terms of both the number of required actuators and the average control energy. Additionally, we show how our method could be applied to control complex networks originating from the human brain and we discover that certain brain cortex regions have a stronger impact on the controllability of network than others.
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