Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is...Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is defined firstly.Because it is an NP-hard combinatorial optimization problem,discrete fireworks algorithm is introduced to search the optimal solution,which is a swarm intelligence algorithm and is improved by the prior information of networks.To verify the effect of improved discrete fireworks algorithm(IDFA),experiments are carried out on various model networks and real power grid.Results show that the proposed IDFA is obviously superior to the benchmark algorithms,and networks suffer more damage when the key node sets obtained by IDFA are removed from the networks.The key node sets found by IDFA contain a large number of non-central nodes,which provides the authors a new perspective that the seemingly insignificant nodes may also have an important impact on the robustness of the network.展开更多
Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make ...Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.展开更多
Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the comm...Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.展开更多
Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a contr...Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.展开更多
The matrix D describing relations of the loops to the nodes in the graph and also the setsof branches based on the independent loops and their matrix Q are defined.The theorem in whichthe product of the loop-node matr...The matrix D describing relations of the loops to the nodes in the graph and also the setsof branches based on the independent loops and their matrix Q are defined.The theorem in whichthe product of the loop-node matrix D multiplied by the incidence matrix A<sub>a</sub> is equal to matrix Qis put forward and proved.The admittance matrix Y<sub>lc</sub> of the sets of the branches is defined and it isassumed that the vector V<sub>lc</sub> of voltage of the sets of branches to be a calculative quantity.The equa-tion of the sets of branches is derived and the analysis method of the sets of branches based on theindependent loops in the electric network is presented.展开更多
在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol...在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol Based on Link Prediction and Energy Sensing,ELPOR).该协议综合考虑节点能量和各节点之间链路连接的概率,从潜在的候选转发集中选择一个中继节点,以实现能量的高效利用和数据的可靠传输。仿真结果表明,该协议能够有效均衡网络能耗、提高吞吐量和延长网络生存周期。展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61502522。
文摘Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is defined firstly.Because it is an NP-hard combinatorial optimization problem,discrete fireworks algorithm is introduced to search the optimal solution,which is a swarm intelligence algorithm and is improved by the prior information of networks.To verify the effect of improved discrete fireworks algorithm(IDFA),experiments are carried out on various model networks and real power grid.Results show that the proposed IDFA is obviously superior to the benchmark algorithms,and networks suffer more damage when the key node sets obtained by IDFA are removed from the networks.The key node sets found by IDFA contain a large number of non-central nodes,which provides the authors a new perspective that the seemingly insignificant nodes may also have an important impact on the robustness of the network.
基金supported by the National Science Foundation of China(61333009,61473317,61433002,61521063,61590924,61673366)the National High Technology Research and Development Program of China(2015AA043102)
文摘Structural controllability is critical for operating and controlling large-scale complex networks. In real applications, for a given network, it is always desirable to have more selections for driver nodes which make the network structurally controllable. Different from the works in complex network field where structural controllability is often used to explore the emergence properties of complex networks at a macro level,in this paper, we investigate it for control design purpose at the application level and focus on describing and obtaining the solution space for all selections of driver nodes to guarantee structural controllability. In accord with practical applications,we define the complete selection rule set as the solution space which is composed of a series of selection rules expressed by intuitive algebraic forms. It explicitly indicates which nodes must be controlled and how many nodes need to be controlled in a node set and thus is particularly helpful for freely selecting driver nodes. Based on two algebraic criteria of structural controllability, we separately develop an input-connectivity algorithm and a relevancy algorithm to deduce selection rules for driver nodes. In order to reduce the computational complexity,we propose a pretreatment algorithm to reduce the scale of network's structural matrix efficiently, and a rearrangement algorithm to partition the matrix into several smaller ones. A general procedure is proposed to get the complete selection rule set for driver nodes which guarantee network's structural controllability. Simulation tests with efficiency analysis of the proposed algorithms are given and the result of applying the proposed procedure to some real networks is also shown, and these all indicate the validity of the proposed procedure.
基金supported by the National Natural Science Foundation of China(71271018)
文摘Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.
文摘Design of control strategies for gene regulatory networks is a challenging and important topic in systems biology. In this paper, the problem of finding both a minimum set of control nodes (control inputs) and a controller is studied. A control node corresponds to a gene that expression can be controlled. Here, a Boolean network is used as a model of gene regulatory networks, and control specifications on attractors, which represent cell types or states of cells, are imposed. It is important to design a gene regulatory network that has desired attractors and has no undesired attractors. Using a matrix-based representation of BNs, this problem can be rewritten as an integer linear programming problem. Finally, the proposed method is demonstrated by a numerical example on a WNT5A network, which is related to melanoma.
文摘The matrix D describing relations of the loops to the nodes in the graph and also the setsof branches based on the independent loops and their matrix Q are defined.The theorem in whichthe product of the loop-node matrix D multiplied by the incidence matrix A<sub>a</sub> is equal to matrix Qis put forward and proved.The admittance matrix Y<sub>lc</sub> of the sets of the branches is defined and it isassumed that the vector V<sub>lc</sub> of voltage of the sets of branches to be a calculative quantity.The equa-tion of the sets of branches is derived and the analysis method of the sets of branches based on theindependent loops in the electric network is presented.
文摘在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol Based on Link Prediction and Energy Sensing,ELPOR).该协议综合考虑节点能量和各节点之间链路连接的概率,从潜在的候选转发集中选择一个中继节点,以实现能量的高效利用和数据的可靠传输。仿真结果表明,该协议能够有效均衡网络能耗、提高吞吐量和延长网络生存周期。
