Optimal control of weighted networks based on node connection strength

Controllability and modularity of complex networks

On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors

Selective removal of certain subgraphs called motifs based on the spread function value is one of the most powerful approaches to curb the overall influence spread in any complex network. In this paper, we first prove that any general spread function preserves both monotonicity and submodularity properties even under motif removal operations. Next, we propose a scoring mechanism as a novel spread function that quantifies the relative importance of a given motif within the overall influence spread dynamics on the complex network. We design a novel algorithm that eliminates motifs with high spread scores to curb influence spread. We evaluate the performance of our proposed spread control algorithm using simulation experiments in the context of 3-node motifs called feed forward loops (FFLs) in both real and synthetic network topologies. We demonstrate that high-scoring motifs intercept a high number of short paths from the pre-assigned source and sinks, because of which their elimination results in a significant effect on curbing the influence spread. Furthermore, we empirically evaluate the run-time and cost versus performance trade-off of the proposed algorithm.

Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in such networks, on the order of billions and higher, which makes it impossible to use conventional network analysis methods. Herein, we employ artificial intelligence (specifically swarm computing), to compute centrality metrics in a completely decentralized fashion. More exactly, we show that by overlaying a homogeneous artificial system (inspired by swarm intelligence) over a complex network (which is a heterogeneous system), and playing a game in the fused system, the changes in the homogeneous system will reflect perfectly the complex network properties. Our method, dubbed Game of Thieves (GOT), computes the importance of all network elements (both nodes and edges) in polylogarithmic time with respect to the total number of nodes. Contrary, the state-of-the-art methods need at least a quadratic time. Moreover, the excellent capabilities of our proposed approach, it terms of speed, accuracy, and functionality, open the path for better ways of understanding and controlling complex networks.

The importance of estimating the region of attraction (ROA) on complex networks has drawn attention very recently. However, challenging problems arise when applying the existing theory to networks with delay. In this article, we estimate the ROA on controlled complex networks with time-varying delay. A delay-independent ROA estimation method is derived at first. This method can deal with general delay and is convenient to use due to its independence of the delay. Accordingly, it may cause conservativeness. To further reduce the conservativeness, delay-dependent ROA estimation is established for networks with small delay by developing a new technique of dealing with the delay. Numerical simulations are provided to verify the theoretical results.

This paper focuses on event-triggered state estimation and control of positive complex networks. An event-triggered condition is provided for discrete-time complex networks by which an event-based state estimator and an estimator-based controller are designed through matrix decomposition technology. Thus, the system is converted to an interval uncertain system. The positivity and the L1-gain stability of complex networks are ensured by resorting to a co-positive Lyapunov function. All conditions are solvable in terms of linear programming. Finally, the effectiveness of the proposed state estimator and controller are verified by a numerical example. The main contributions of this paper are as follows: (i) A positive complex network framework is constructed based on an event-triggered strategy, (ii) a new state estimator and an estimator-based controller are proposed, and (iii) a simple analysis and design approach consisting of a co-positive Lyapunov function and linear programming is presented for positive complex networks.

SummaryThe article mainly studies the fault‐tolerant control of complex networks with semi‐Markov jump topology under hybrid attacks. First, the semi‐Markov process is imported to the topology change of sampled‐data complex networks. Second, by designing a strategy to resist mixed attacks, the system can remain stable even under mixed attacks. Then, some sufficient conditions have been established to guarantee performance through fault‐tolerant control mechanism even if it suffers from actuator failure. Finally, a numerical example and a practical example are used to prove the feasibility of the theory.

Complex dynamic network is a representative model for the interactions of complex system, such as the Internet network, smart grid, and biological network. Many studies have investigated the dynamics in complex networks and control of complex networks. Among these works, an accurate topology of the complex network is an essential prerequisite. Therefore, reconstruction of the complex network topology from measured node dynamics data is important yet challenging. By analyzing and extracting the underlying feature of unweighted and undirected networks, we propose a structured compressive sensing method that reconstructs the topology of complex network globally. Through intensive numerical simulations of an artificial small-world network, an artificial scale-free network, and two real networks, we find that the proposed method is efficient for complex network topology reconstruction, and it is also robust against weak stochastic perturbations.

How the node’s vital and tie strength effect rumor spreading on social network

Ranking influential nodes in complex network using edge weight degree based shell decomposition

Controlling complex network is an essential problem in network science and engineering. Recent advances indicate that the controllability of complex network is dependent on the network's topology. Liu and Barabási, et.al speculated that the degree distribution was one of the most important factors affecting controllability for arbitrary complex directed network with random link weights. In this paper, we analysed the effect of degree distribution to the controllability for the deterministic networks with unweighted and undirected. We introduce a class of deterministic networks with identical degree sequence, called (x,y)-flower. We analysed controllability of the two deterministic networks ((1, 3)-flower and (2, 2)-flower) by exact controllability theory in detail and give accurate results of the minimum number of driver nodes for the two networks. In simulation, we compare the controllability of (x,y)-flower networks. Our results show that the family of (x,y)-flower networks have the same degree sequence, but their controllability is totally different. So the degree distribution itself is not sufficient to characterize the controllability of deterministic networks with unweighted and undirected.

Complex fluid network optimization and control integrative design based on nonlinear dynamic model

This paper illustrates intelligent control in complex networks by the example of road traffic management. An important goal in road traffic management, which as yet has been achieved only very partially, is to make the move from local traffic management to network level traffic management. The approach to this goal described here is a combination of top-down control and several bottom-up control mechanisms: top-down control consists of off-line preparation of control scenario's that apply to certain traffic patterns. The bottom-up mechanisms rely on emergent behavior of multi-agent systems: one set of agents is formed by the vehicles, a second set is formed by the nodes and links of the road network. The approach can to a certain extent be generalized to other types of complex networks.

Optimizing target control in complex networks using edge-addition cost

The widely used Maximum Matching (MM) method identifies the minimum driver nodes set to control biological and technological systems. Nevertheless, it is assumed in the MM approach that one driver node can send control signal to multiple target nodes, which might not be appropriate in certain complex networks. A recent work introduced a constraint that one driver node can control one target node, and proposed a method to identify the minimum target nodes set under such a constraint. We refer such target nodes to driven nodes. However, the driven nodes may not be uniquely determined. Here, we develop a novel algorithm to classify driven nodes in control categories. Our computational analysis on a large number of biological networks indicates that the number of driven nodes is considerably larger than the number of driver nodes, not only in all examined complete plant metabolic networks but also in several key human pathways, which firstly demonstrate the importance of use of driven nodes in analysis of real-world networks.