Altering control modes of complex networks based on edge removal

Zhang, Xizhe; Li, Qian

doi:10.1016/j.physa.2018.09.146

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Previous work has discovered a bimodality feature in control [6,8,9,10]. For neutral networks with symmetric out-and in-degree distribution, when the average degree k of the network exceeds a critical value k c , n r would follow a bifurcation diagram, which is similar to the pitchfork bifurcation [11,12].…”

Section: Introductionmentioning

confidence: 89%

The network asymmetry caused by the degree correlation and its effect on the bimodality in control

Yu,

Liang,

Wang

et al. 2021

Preprint

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Our ability to control a whole network can be achieved via a small set of driver nodes. While the minimum number of driver nodes needed for control is fixed in a given network, there are multiple choices for the driver node set. A quantity used to investigate this multiplicity is the fraction of redundant nodes in the network, referring to nodes that do not need any external control. Previous work has discovered a bimodality feature characterized by a bifurcation diagram: networks with the same statistical property would stay with equal probability to have a large or small fraction of redundant nodes.Here we find that this feature is rooted in the symmetry of the directed network, where both the degree distribution and the degree correlation can play a role. The in-in and out-out degree correlation will suppress the bifurcation, as networks with such degree correlations are asymmetric under network transpose. The out-in and in-out degree correlation do not change the network symmetry, hence the bimodality feature is preserved. However, the out-in degree correlation will change the critical average degree needed for the bifurcation. Hence by fixing the average degree of networks and tuning out-in degree correlation alone, we can observe a similar bifurcation diagram. We conduct analytical analyses that adequately explain the emergence of bimodality caused by out-in degree correlation. We also propose a quantity, taking both degree distribution and degree correlation into consideration, to predict if a network would be at the upper or lower branch of the bifurcation. As is well known that most real networks are not neutral, our results extend our understandings of the controllability of complex networks.

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Section: Introductionmentioning

confidence: 89%

The network asymmetry caused by the degree correlation and its effect on the bimodality in control

Yu,

Liang,

Wang

et al. 2021

Preprint

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“…Moreover, this led to categorizing nodes in three groups with respect to their contribution within all MDSs. These groups are called critical, intermittent, and redundant [43][44][45]. A critical node exists in all MDSs, i.e., the critical nodes are always driver nodes.…”

Section: Analyzing Multiple Driver Node Setsmentioning

confidence: 99%

“…The aforementioned categories of driver nodes (critical, intermittent, and redundant) form two control regimes, namely centralized and distributed [43,44]. A centralized control regime indicates redundant nodes are dominant in a network.…”

Section: Distributed and Centralized Control Regimesmentioning

confidence: 99%

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Identifying and using driver nodes in temporal networks

Ravandi

Mili

Springer

2019

Journal of Complex Networks

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In many approaches developed for defining complex networks, the main assumption is that the network is in a relatively stable state that can be approximated with a fixed topology. However, in several applications, this approximation is not adequate because (a) the system modelled is dynamic by nature, and (b) the changes are an essential characteristic that cannot be approximated. Temporal networks capture changes in the topology of networks by including the temporal information associated with their structural connections, that is, links or edges. Here, we focus on controllability of temporal networks, that is, the study of steering the state of a network to any desired state at deadline $t_f$ within $\Delta t=t_f - t_0$ steps through stimulating key nodes called driver nodes. Recent studies provided analytical approaches to find a maximum controllable subspace for an arbitrary set of driver nodes. However, finding the minimum number of driver nodes $N_c$ required to reach full control is computationally prohibitive. In this article, we propose a heuristic algorithm that quickly finds a suboptimal set of driver nodes with size $N_s \geq N_c$. We conduct experiments on synthetic and real-world temporal networks induced from ant colonies and e-mail communications of a manufacturing company. The empirical results in both cases show the heuristic algorithm efficiently identifies a small set of driver nodes that can fully control the networks. Also, as shown in the case of ants’ interactions networks, the driver nodes tend to have a large degree in temporal networks. Furthermore, we analyze the behavior of driver nodes within the context of their datasets, through which, we observe that queen ants tend to avoid becoming a driver node.

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“…Here, we utilized network controllability theory (31)(32)(33)(34)(35)(36) to analyze the protein-protein interaction (PPI) network of MYCN. We identified possible potential drug targets of the MYCN regulatory network and evaluated the importance of these potential targets with several existing databases.…”

Section: Introductionmentioning

confidence: 99%

Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN

Pan

Zhu

et al. 2021

Front. Oncol.

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BackgroundMYCN is an oncogenic transcription factor of the MYC family and plays an important role in the formation of tissues and organs during development before birth. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets.MethodsWe utilized network controllability theory, a recent developed powerful tool, to identify the potential drug target around MYCN based on Protein-Protein interaction network of MYCN. First, we constructed a Protein-Protein interaction network of MYCN based on public databases. Second, network control analysis was applied on network to identify driver genes and indispensable genes of the MYCN regulatory network. Finally, we developed a novel integrated approach to identify potential drug targets for regulating the function of the MYCN regulatory network.ResultsWe constructed an MYCN regulatory network that has 79 genes and 129 interactions. Based on network controllability theory, we analyzed driver genes which capable to fully control the network. We found 10 indispensable genes whose alternation will significantly change the regulatory pathways of the MYCN network. We evaluated the stability and correlation analysis of these genes and found EGFR may be the potential drug target which closely associated with MYCN.ConclusionTogether, our findings indicate that EGFR plays an important role in the regulatory network and pathways of MYCN and therefore may represent an attractive therapeutic target for cancer treatment.

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Altering control modes of complex networks based on edge removal

Cited by 6 publications

References 28 publications

The network asymmetry caused by the degree correlation and its effect on the bimodality in control

The network asymmetry caused by the degree correlation and its effect on the bimodality in control

Identifying and using driver nodes in temporal networks

Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN

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