Publications

In this paper, we propose a decentralized adaptive pinning control scheme for synchronization of undirected networks using a local adaptive strategy to determine both coupling strengths and feedback gains. By applying this local adaptive strategy, we show that the network can achieve synchronization with small coupling strengths and feedback gains. We provide some simulations to verify and illustrate the theoretical results.

Traffic fluctuation has so far been studied on unweighted networks. However many real traffic systems are better represented as weighted networks, where nodes and links are assigned a weight value representing their physical properties such as capacity and delay. Here we introduce a general random diffusion (GRD) model to investigate the traffic fluctuation in weighted networks, where a random walk's choice of route is affected not only by the number of links a node has, but also by the weight of individual links. We obtain analytical solutions that characterise the relation between the average traffic and the fluctuation through nodes and links. Our analysis is supported by the results of numerical simulations. We observe that the value ranges of the average traffic and the fluctuation, through nodes or links, increase dramatically with the level of heterogeneity in link weight. This highlights the key role that link weight plays in traffic fluctuation and the necessity to study traffic fluctuation on weighted networks.

This paper applies a recurrent higher-order neural network for sliding-mode pinning control of complex networks for achieving trajectory tracking. This control strategy does not require having the same coupling strength for all node connections on the network. The tracking effectiveness and dynamical behavior of the controlled network is illustrated via simulations.

No abstract is provided for this article.

A necessary and sufficient condition is derived for the controllability of Kronecker product networks, where the factor networks are general directed graphs. The condition explicitly illustrates how the controllability of the factor networks affects the controllability of the composite network. For the special case where at least one factor network is diagonalizable, an easily-verifiable condition is explicitly expressed. Furthermore, the controllability of higher-dimensional multi-agent systems is revisited, revealing that some controllability criterion reported in the literature does not hold. Consequently, a modified necessary and sufficient condition is established. The effectiveness of the new conditions is demonstrated through several examples.

The COVID-19 pandemic brought not only global devastation but also an unprecedented infodemic of false or misleading information that spread rapidly through online social networks. Network analysis plays a crucial role in the science of fact-checking by modeling and learning the risk of infodemics through statistical processes and computation on mega-sized graphs. This article proposes MEGA, Machine Learning-Enhanced Graph Analytics, a framework that combines feature engineering and graph neural networks to enhance the efficiency of learning performance involving massive graphs. Infodemic risk analysis is a unique application of the MEGA framework, which involves detecting spambots by counting triangle motifs and identifying influential spreaders by computing the distance centrality. The MEGA framework is evaluated using the COVID-19 pandemic Twitter dataset, demonstrating superior computational efficiency and classification accuracy.

This paper is concerned with the realizability problem of n ‐port resistive networks that contain 2n terminals. A necessary and sufficient condition for any real symmetric matrix to be realizable as the admittance of an n ‐port resistive network containing 2n terminals is obtained. This condition is based on the existence of a parameter matrix. Furthermore, the values of the elements are expressed in terms of the entries of the admittance matrix and the parameter matrix. Finally, a numerical example is used to illustrate the results. Copyright © 2013 John Wiley & Sons, Ltd.

Current resilient consensus algorithms deal with multiagent networks with lower order nodal dynamics where each agent excludes certain extreme values from its neighbors. This does not scale well in general networks. In this article, a new type of resilient consensus algorithm based on distributed attack isolation (DAI-RC) is proposed for general higher order networks. In the DAI-RC algorithm, the evolution of each normal agent can avoid the influence from those neighbors which are isolated as victims of attack. To characterize a feasible communication topology to isolate attacked agents, a notion of <i>graph isolability</i> is proposed based on which a sufficient condition to isolate the attacked agents is presented. Simulations are finally provided to illustrate the effectiveness of the proposed algorithm.

No abstract is provided for this article.

In this paper, a coordinated tracking control problem for a multi-agent system under noise perturbations is considered over a leader-follower communication network. Two control gain functions are designed where the first one is used to attenuate the effect of noise and the second to stabilize the system. Using tools from the algebraic graph theory and stochastic analysis, it is proved that the followers can track the leader in mean square over a time-invariant network. The results are then extended to the scenario of a time-varying network.

Let us model the network by an undirected graph G, in which each edge fails independently with probability q ∈ [ 0 , 1 ] , the all-terminal reliability of the network is the probability that the graph G remains connected and it is one of the important measure for the invulnerability of the network. Clearly, the all-terminal reliability can be written as a polynomial in either q. Theoretically, it has been proved that calculating all-terminal reliability for planar networks is #P-complete. Up now we cannot find any results for some special classes of the planar networks. This paper studies the all-terminal reliability of the two-tree networks and its generalizations. A linear algorithm for calculating the reliability of a planar two-tree networks is proposed, with algorithmic complexity O ( n ) , where n is the number of steps. Then both the uniformly-most reliable network model and the uniformly-worst reliable network model are determined by using the algorithm. Finally, a polynomial algorithm for planar two-connected networks is proposed based on an expended algorithm and the corresponding uniformly-most reliable network model and uniformly-worst reliable network model are also presented, with computational complexity O ( n ) , where n is the number of steps.