Signal propagation in complex networks

Robustness and Optimization of Complex Networks: Reconstructability, Algorithms and Modeling

Link prediction in multilayer networks using weighted reliable local random walk algorithm

Most work on convention propagation have focused on single-layer networks. This work a) highlights the need for studying convention propagation in multi-layer networks, b) presents results comparing the speed of convention propagation in single-layer vs. multi-layer networks and c) demonstrates the role of influencer agents in convention propagation in multi-layer networks.

Information dissemination has become one of the most important services of communication networks. Modeling the diffusion of information through such networks is crucial for our modern information societies. In this work, novel models, segregating between useful and malicious types of information, are introduced, in order to better study Information Dissemination Dynamics (IDD) in wireless complex communication networks, and eventually allow taking into account special network features in IDD. According to the proposed models, and inspired from epidemiology, we investigate the IDD in various complex network types through the use of the Susceptible-Infected (SI) paradigm for useful information dissemination and the Susceptible-Infected-Susceptible (SIS) paradigm for malicious information spreading. We provide analysis and simulation results for both types of diffused information, in order to identify performance and robustness potentials for each dissemination process with respect to the characteristics of the underlying complex networking infrastructures. We demonstrate that the proposed approach can generically characterize IDD in wireless complex networks and reveal salient features of dissemination dynamics in each network type, which could eventually aid in the design of more advanced, robust, and efficient networks and services.

Complex network analysis which can be represented as graph has gained much interest from researchers recently. Analysis derived from complex network leading to a discovery of important group or community lies within the network. It imposes a significant challenge to computer scientists, physicists, and sociologists alike, to identify and discover the true meaning of community for complex network. Different community detection algorithms have been proposed in different perspective of almost similar aim of identifying the community. In this paper, we apply the modularity measurement on complex network and test the strengthness of community found by algorithm proposed. The main study focuses on the importance of having robust algorithm in detecting communities in different type of complex network. Experimental results show that the method is able to successfully separate community by achieving an ideal modularity value.

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

As necessities in people's lives, the stability and quality safety of the production and circulation of agricultural products are widely of concern to the whole society. With the development of economic globalization, networking and informatization, the structure and dynamic behaviour of the agricultural product supply chain are becoming increasingly complex. A concept increasingly central to supply chain theory is sustainability. As the frontier of network science, complex network theory provides a new method to study complex real networks. This paper adopts the complex network theory to explore the formation and evolution mechanism of the agricultural product supply chain network and the risk propagation mechanism on the supply chain network through modelling and simulation. On this basis, it proposes the control strategy of risk propagation. The research in this paper can enrich the results of complex network theory in the field of supply chain based on ecological theory. The network model construction can reveal the formation and evolution mechanism of the agricultural product supply chain network. The analysis of risk dynamics can provide a theoretical basis and practical guidance for controlling risk propagation of agricultural product supply chain networks.

Link prediction is one of the methods of social network analysis. Bipartite networks are a type of complex network that can be used to model many natural events. In this study, a novel similarity measure for link prediction in bipartite networks is presented. Due to the fact that classical social network link prediction methods are less efficient and effective for use in bipartite network, it is necessary to use bipartite network-specific methods to solve this problem. The purpose of this study is to provide a centralized and comprehensive method based on the neighborhood structure that performs better than the existing classical methods. The proposed method consists of a combination of criteria based on the neighborhood structure. Here, the classical criteria for link prediction by modifying the bipartite network are defined. These modified criteria constitute the main component of the proposed similarity measure. In addition to low simplicity and complexity, this method has high efficiency. The simulation results show that the proposed method with a superiority of 0.5% over MetaPath, 1.32% over FriendLink, and 1.8% over Katz in thef-measure criterion shows the best performance.

Nonnegative matrix factorization (NMF) has become a powerful model for community discovery in complex networks. Existing NMF-based methods for community discovery often factorize the corresponding adjacent matrix of complex networks to obtain its community indicator matrix. However, the adjacent matrix cannot represent the global structure feature of complex networks very well, and this leads to the performance degradation of community discovery. Besides, most of existing methods are not robust and scalable enough, so they are not effective to deal with complex networks with noises and large scales. Aiming at these problems above, in this paper we propose a method for community discovery using distributed robust NMF with SimRank similarity measure. This method selects SimRank measure to construct the feature matrix, which can more accurately represent the global structure feature of complex networks. To improve the robustness, we select $$\ell _{2,1}$$ norm instead of the widely used Frobenius norm to construct its NMF-based community discovery model. In addition, to improve the scalability, we implement its key components by using MapReduce distributed computing framework, including computing SimRank feature matrix and iteratively solving the NMF-based model for community discovery. We conduct extensive experiments on several typical complex networks. The results show that our method has better performance and robustness than other representative NMF-based methods for community discovery. Moreover, our method presents good scalability and hence can be used to discover communities in the large-scale complex networks.

The ability of weak signal detection and transduction of neurons is of great importance. Although the subject hotly debated in single neuron and complex networks [1], it has been partly addressed in feedforward networks [2,3]. In our previous work [2], we determined the conditions for weak rhythmic signal propagation through a feedforward network where the neurons are constructed with detailed biophysical modeling approaches. It is shown that the optimal propagation of weak rhythmic signals through feedforward neuronal networks depends significantly on the level of intrinsic noise, the forcing frequency and the inter-layer link density. Here, we used the same network structure in [2], which involves 10 layers with N=200 neurons in each one and 10% inter-layer link density (Fig1a). For synaptic transmission, alpha-synapse type is used to model the conductance variations in the post-synaptic neuron resulting from the binding of neurotransmitter released from the pre-synaptic neuron. Release time of neurotransmitters τsyn and coupling constant gsyn are critical parameters of this type of synapses. A measure Qi is chosen to quantify the signal transmission efficiency, which gives the existence of the input signal frequency at any layers’ output, similar to Signal to Noise Ratio (SNR). Model details and methods can be found in [2]. To extend our findings, we first investigated the role of fraction of forced neurons forming the first layer on the propagation of weak rhythmic activity. We consider different fractions of periodically forced neurons (chosen randomly) and compute Q10 (Fourier coefficients of output layer) over a broad frequency range (Fig 1b). Evidently, the larger the fraction of neurons subject to the weak forcing, the better the outreach of the signal through the network. This can be appreciated most clearly for the optimal angular forcing frequency, ω≈0.4ms–1[2]. Interestingly, the depicted curves show only marginal improvement in Q10 if more than 50% of the neurons forming the first layer are forced. This finding suggests that the propagation of weak signals across feedforward neuronal networks is rather robust to variations in the coverage of the initial input. We also examined how synaptic mechanisms effect the propagation of weak rhythmic activity through the network. For this purpose, a periodic sinusoidal force with the optimal angular frequency ω≈0.4ms–1 is introduced to all neurons in the first layer as shown in Fig​Fig1a,1a, then we computed Q10/Q1gain factor with respect to systematically changed values of synaptic parameters. Results are presented in Fig​Fig1c.1c. It is clearly seen that signal transmission can be modulated by the synaptic transmission parameters. There is an optimal τsyn range, i.e. 1.5 =1 for gsyn >0.4 nS, indicating the efficient transmission of the weak signal. The underlying mechanism can be understood by comparing synaptic and membrane time constants with respect to each other, because they together determine the operation mode of neurons, i.e. coincidence detector or temporal integrator [3]. Fig 1c also shows that weak signal can be transmitted to the output layer for the coupling constants of gsyn >0.4 nS regardless ofτsyn, implying that the synaptic coupling must be higher than a critical value. Figure 1 (a) Schematic illustration of a 10 layer feedforward network, (b) Fourier coefficient Q10 in dependence on ω for different fractions of forced neurons forming the first layer, (c) The dependence of the gain factor Q10 / Q1with respect to the synaptic ...

Collaborative response to disruption propagation (CRDP) in cyber-physical systems and complex networks

Link prediction is usually studied in unweighted networks, while ignoring the importance of weights in complex networks. In this paper, an algorithm for link prediction with link weight assignments is proposed for unweighted networks. First, weights of links are assigned based on the node degree and clustering coefficient, which describe the closeness of two nodes in complex networks; second, the weighted form of similarity index instead of the unweighted form are applied for link prediction; finally, we compare the performance of link predicitions in networks with or without weight assignments. Experiments on four commonly used complex network data sets have been conducted, and the results show that weights of links play an important role in the complex networks, and by assigning weights to links accordingly the algorithm proposed can improve the link prediction performance.

We investigate the quantum synchronization phenomenon of the complex network constituted by coupled optomechanical systems and prove that the unknown identical quantum states can be shared or distributed in the quantum network even though the topology is varying. Considering a channel constructed by quantum correlation, we show that quantum synchronization can sustain and maintain high levels in Markovian dissipation for a long time. We also analyze the state-sharing process between two typical complex networks, and the results predict that linked nodes can be directly synchronized, but the whole network will be synchronized only if some specific synchronization conditions are satisfied. Furthermore, we give the synchronization conditions analytically through analyzing network dynamics. This proposal paves the way for studying multi-interaction synchronization and achieving effective quantum information processing in a complex network.

Critical node identification is the precondition for exploring complex networks, which is a graph‐based optimal search problem in principle. Classical methods, hindered by limited parallel processing capabilities, often grapple with high computational demands and inefficiency. Recent studies have leveraged quantum walk (QW) to tackle such challenges. However, existing QW‐based approaches are constrained by graph structures and prone to recurrent traversal issues. Being different, a novel forward discrete‐time quantum walk (F‐DTQW) method is proposed, which can traverse without redundant visits by prohibiting reverse and lateral propagations. The proposed method significantly enhances the efficiency of critical node identification and is applicable to arbitrary complex networks. The proposed method is validated on typical complex networks such as small‐world, scale‐free, and directed networks, and calculates the transition probability of nodes, corresponding to the centrality measurement index of transition connectivity (TC), which expands from the betweenness centrality (BC) index. The time complexity of F‐DTQW is also analyzed, which is reduced from O ( N 3 ) to O ( N 2 ) compared to the classical method.

We propose an influence-based aggregative learning framework that facilitates the emergence of social norms in complex networks and investigate how a norm converges by learning through iterated local interactions in a coordination game. In society, humans decide to coordinate their behavior not only by exchanging information but also on the basis of norms that are often individually derived from interactions without a centralized authority. Coordination using norms has received much attention in studies of multi-agent systems. In addition, because agents often work as delegates of humans, they should have mental models about how to interact with others and incorporate differences of opinion. Because norms make sense only when all or most agents have the same one and they can expect that others will follow, it is important to investigate the mechanism of norm emergence through learning with local and individual interactions in agent society. Our method of norm learning borrows from the opinion aggregation process while taking into account the influence of local opinions in tightly coordinated human communities. We conducted experiments showing how our learning framework facilitates propagation of norms in a number of complex agent networks.