Search

In this paper, we investigate a novel finite-time median-related group consensus problem, where the finial consensus value can be identified as a desired function of the median of initial states instead of the much studied average value. The underlying communication topology is modelled by a weighted dynamical directed network. A distributed control protocol is firstly introduced to ensure that the agents can reach a median-related consensus in finite time in a collaboration network, meaning that all edge-weights of the communication network are non-negative. We then generalise the results to cooperation–competition networks, where the communication network is divided into predetermined collaboration subnetworks allowing possibly negative weights. Effective group control protocols are designed to guarantee the median-related group consensus in finite time. Finally, numerical simulations are presented to illustrate the availability of our theoretical results. [ABSTRACT FROM AUTHOR]

Abstract This letter considers the resilient consensus problem for switched multi-agent systems composed of continuous-time and discrete-time subsystems. We propose a switched filtering strategy for cooperative nodes based upon available local information, withstanding the threat of non-cooperative nodes. We provide conditions that guarantee resilient consensus in the presence of locally bounded Byzantine nodes in directed networks under arbitrary switching. Resilient scaled consensus and resilient scaled formation generation problems for switched multi-agent systems are solved as generalizations. Simulations are also provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Robustness against attacks serves as evidence for complex network structures and failure mechanisms that lie behind them. Most often, due to detection capability limitation or good disguises, attacks on networks are subject to false positives and false negatives, meaning that functional nodes may be falsely regarded as compromised by the attacker and vice versa. In this work, we initiate a study of false positive/negative effects on network robustness against three fundamental types of attack strategies, namely, random attacks (RA), localized attacks (LA), and targeted attack (TA). By developing a general mathematical framework based upon the percolation model, we investigate analytically and by numerical simulations of attack robustness with false positive/negative rate (FPR/FNR) on three benchmark models including Erdős-Rényi (ER) networks, random regular (RR) networks, and scale-free (SF) networks. We show that ER networks are equivalently robust against RA and LA only when FPR equals zero or the initial network is intact. We find several interesting crossovers in RR and SF networks when FPR is taken into consideration. By defining the cost of attack, we observe diminishing marginal attack efficiency for RA, LA, and TA. Our finding highlights the potential risk of underestimating or ignoring FPR in understanding attack robustness. The results may provide insights into ways of enhancing robustness of network architecture and improve the level of protection of critical infrastructures. [ABSTRACT FROM AUTHOR]

This paper studies the problem of resilient consensus in continuous-time multiagent systems against potential malicious agents. Network topology conditions have been developed to ensure asymptotic consensus of cooperative agents in the network under multi-hop communication and path-dependent heterogeneous delays. We develop two new distributed protocols based on the idea of weighted mean subsequence reduced algorithm to address scalar resilient consensus and vector resilient consensus problems in path-weighted directed networks leveraging on the capability of multi-hop communication. Our frameworks are flexible in that both linear and general nonlinear network coupling functions are featured. Resilient consensus problem for dynamical agents with higher-order integrators is solved as a byproduct. We illustrate the error introduced by the resilience mechanisms when the network is free of malicious agents through numerical simulations. [ABSTRACT FROM AUTHOR]

Resilience or recovery of multi-agent systems against attacks is believed to be one of the most important topics in control and management of complex systems. Previous studies have mainly focused on the random failure and intentional attack targeted on node degrees, whereas consensus recovery is much less investigated. In this paper, two kinds of targeted attacks are introduced and corresponding recovery strategies are proposed to address the consensus recovery of continuous-time nonlinear multi-agent systems under directed topologies against subsequent attacks. The cut-nodes and cut-edges are chosen as the targets of attack, and the multi-agent system becomes disconnected when they are deleted from the system. The neighbor nodes of the cut-node and end nodes of the cut-edge are used to recover the connectivity of the network. It is shown that our proposed strategies can significantly improve the convergence speed of reaching consensus of the resulting multi-agent systems. A cost function is also proposed to compare different recovery strategies. [ABSTRACT FROM AUTHOR]

The geometry of complex networks has a close relationship with their structure and function. In this article, we investigate Gromov-hyperbolicity of inhomogeneous random networks modeled by the Chung-Lu modelG(w). When the maximum expected degreewmaxand minimum expected degreewminsatisfywmax ≤ 21/3wmin, we prove that for any positive δ,G(w) has a positive probability of containing δ-fat triangles asn → ∞. Our numerical simulations illustrate this non-hyperbolicity ofG(w) for power law degree distributions among others. [ABSTRACT FROM AUTHOR]

During an epidemic outbreak in a human population, susceptibility to infection can be reduced by raising awareness of the disease. In this paper, we investigate the effects of three forms of awareness (i.e., contact, local, and global) on the spread of a disease in a random network. Connectivity-correlated transmission rates are assumed. By using the mean-field theory and numerical simulation, we show that both local and contact awareness can raise the epidemic thresholds while the global awareness cannot, which mirrors the recent results of Wu et al. The obtained results point out that individual behaviors in the presence of an infectious disease has a great influence on the epidemic dynamics. Our method enriches mean-field analysis in epidemic models. [ABSTRACT FROM AUTHOR]

Our main interest in this paper is the large dicliques in a directed inhomogeneous random graph modelG(n,α, Φ) onnvertices, which has power-law out/in-degree distributions with scaling exponent α>0 and community structures involved in the homophyly matrix Φ. We show that there is a major difference in the size of the largest diclique ωd(G(n,α, Φ)) between the case α<2 and α>2 with an intermediate result for α=2. In addition, we show that a simple algorithm with high probability finds a large diclique of size ωd(G(n,α, Φ)) in a polynomial time. Our simulation results reveal that the connections between different subcommunities are essential for the formation of large clusters in the networks. [ABSTRACT FROM PUBLISHER]

We addressL1group consensus problems of networked multi-agent systems with fixed directed information exchange in this article. A discrete-time protocol is designed to solve theL1group consensus problems via general stochastic inputs. Under some mild assumptions, we derive sufficient conditions to guarantee theL1group consensus based on algebraic graph theory and ergodicity theory for finite Markov chains. Moreover, the boundedness of the expected infinite sum of the inputs ensures the boundedness of the expected trajectory. Numerical simulations instantiating the inputs as varied independent and identically distributed random processes as well as autoregressive processes are presented to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM PUBLISHER]

In this article, we study finite-time state consensus problems for continuous multi-agent systems with nonlinear protocols. Building on the theory of finite-time Lyapunov stability, we propose sufficient criteria which guarantee the system to reach a consensus in finite time, provided that the underlying directed network contains a spanning tree. Novel finite-time consensus protocols are introduced as examples for applying the criteria. Simulations are also presented to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

Social networks describe social interactions between people, which are often modeled by intersection graphs. In this paper, we propose an intersection graph model that is induced by adding a sparse random bipartite graph to a given bipartite graph. Under some mild conditions, we show that the vertex–isoperimetric number and the edge–isoperimetric number of the randomly perturbed intersection graph on n vertices are Ω (1 / ln n) asymptomatically almost surely. Numerical simulations for small graphs extracted from two real-world social networks, namely, the board interlocking network and the scientific collaboration network, were performed. It was revealed that the effect of increasing isoperimetric numbers (i.e., expansion properties) on randomly perturbed intersection graphs is presumably independent of the order of the network. [ABSTRACT FROM AUTHOR]