Optimizing Driver Nodes for Structural Controllability of Temporal Networks

Srighakollapu, Manikya Valli; Kalaimani, Rachel Kalpana; Pasumarthy, Ramkrishna

doi:10.1109/tcns.2021.3106454

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…By employing the property of the cut between two disjoint node sets in a graph [28], the submodularity of the set function (3) can be obtained [26]. Here we rely on the property of submodularity to employ pruning strategies to eliminate subsets of the search space that are unlikely to yield the maximum increment.…”

Section: Online Time-accelerated Heuristic Algorithmmentioning

confidence: 99%

“…In recent years, several algorithms have been proposed to address the problem. Ravandi et al developed a heuristic algorithm to yield multiple sets of driver nodes [25] and Srighakollapu et al presented a greedy algorithm to obtain an approximately optimal solution [26]. These algorithms have shown promising results and important applications in some systems.…”

Section: Introductionmentioning

confidence: 99%

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Detecting the driver nodes of temporal networks

Qin

Duan

2023

New J. Phys.

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Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks.

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Section: Online Time-accelerated Heuristic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting the driver nodes of temporal networks

Qin

Duan

2023

New J. Phys.

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show abstract

“…In recent years, a particular focus from the networks science community has been on temporal networks (Holme & Saramäki, 2012). In this context, structural controllability of temporal networks considers those modeled by linear time-varying systems whose structural pattern remains unchanged, and their realization may vary over time (Pósfai & Hövel, 2014;Srighakollapu, Kalaimani, & Pasumarthy, 2021). The authors of Pósfai and Hövel (2014) investigate the controllability of systems with of the dynamics' timescale comparable with the changes in the network timescale.…”

Section: Temporal Networkmentioning

confidence: 99%

“…They present analytical and computational tools to study controllability based on temporal network characteristics. The work in Srighakollapu et al (2021) presents conditions for structural controllability of temporal networks that change topology and edge weights with time.…”

Section: Temporal Networkmentioning

confidence: 99%