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Abstract Many real-world phenomena can be modelled as spatial networks where nodes have distinct geographical location. Examples include power grids, transportation networks and the Internet. This paper focuses on optimizing the synchronizability of spatial networks. We consider the eigenratio of the Laplacian Matrix of the connection graph as a metric measuring the synchronizability of the network and develop an efficient rewiring mechanism to optimize the topology of the network for synchronizability, i.e., minimizing the eigenratio. The Euclidean distance between two connected nodes is considered as their connection weights, and the sum of all connection weights is defined as the network cost. The proposed optimization algorithm constructs spatial networks with a certain number of nodes and a predefined network cost. We also study the topological properties of the optimized networks. This algorithm can be used to construct spatial networks with optimal synchronization properties. Highlights • A model is proposed to construct random spatial networks. • A rewiring-based method is proposed to improve synchronizability of the networks. • Optimized networks have increased local and global efficiency. [ABSTRACT FROM AUTHOR]

Controlling dynamics of networked systems to a reference state, known as pinning control, has many applications in science and engineering. In this paper, we introduce a method for effective augmentation of networked systems, while also providing high levels of pinning controllability for the final augmented network. The problem is how to connect a sub-network to an already existing network such that the pinning controllability is maximised. We consider the eigenratio of the augmented Laplacian matrix as a pinning controllability metric, and use graph perturbation theory to approximate the influence of edge addition on the eigenratio. The proposed metric can be effectively used to find the inter-network links connecting the disjoint networks. Also, an efficient link rewiring approach is proposed to further optimise the pinning controllability of the augmented network. We provide numerical simulations on synthetic networks and show that the proposed method is more effective than heuristic ones. [ABSTRACT FROM AUTHOR]

This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human–human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others. [ABSTRACT FROM AUTHOR]

In this paper we study synchronizability of two multiplex cortical networks: whole-cortex of hermaphrodite C. elegans and posterior cortex in male C. elegans . These networks are composed of two connection layers: network of chemical synapses and the one formed by gap junctions. This work studies the contribution of each layer on the phase synchronization of non-identical spiking Hindmarsh–Rose neurons. The network of male C. elegans shows higher phase synchronization than its randomized version, while it is not the case for hermaphrodite type. The random networks in each layer are constructed such that the nodes have the same degree as the original network, thus providing an unbiased comparison. In male C. elegans , although the gap junction network is sparser than the chemical network, it shows higher contribution in the synchronization phenomenon. This is not the case in hermaphrodite type, which is mainly due to significant less density of gap junction layer (0.013) as compared to chemical layer (0.028). Also, the gap junction network in this type has stronger community structure than the chemical network, and this is another driving factor for its weaker synchronizability. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we investigated the structural properties of the ultra high voltage power transmission network of Iran. We modeled the power grid as a network with 105 nodes and 142 connection links. We found that the Iranian power grid displays a relatively moderate clustering coefficient–much larger than that of corresponding random networks–and small characteristics path length comparable to that of corresponding random networks; i.e. the power grid is a small-world network with exponential degree distribution. Global efficiency was considered as an indicator of grid’s performance and the influence of random and intentional nodal failures on the efficiency was investigated. We also studied the influence of cascaded failures on the largest connected component of the network. The power grid was vulnerable against cascaded failures, which should be considered serious in redesigning the network topology. [Copyright &y& Elsevier]

Abstract: In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts–Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts–Strogatz networks could not significantly change the consensus profile. [Copyright &y& Elsevier]

Abstract: In this paper the robustness of network synchronizability against random deletion of nodes, i.e. errors, in dynamical scale-free networks was studied. To this end, two measures of network synchronizability, namely, the eigenratio of the Laplacian and the order parameter quantifying the degree of phase synchrony were adopted, and the synchronizability robustness on preferential attachment scale-free graphs was investigated. The findings revealed that as the network size decreases, the robustness of its synchronizability against random removal of nodes declines, i.e. the more the number of randomly removed nodes from the network, the worse its synchronizability. We also showed that this dependence of the synchronizability on the network size is different with that in the growing scale-free networks. The profile of a number of network properties such as clustering coefficient, efficiency, assortativity, and eccentricity, as a function of the network size was investigated in these two cases, growing scale-free networks and those with randomly removed nodes. The results showed that these processes are also different in terms of these metrics. [Copyright &y& Elsevier]

Many real-world phenomena can be modeled as networked systems. Some of these systems consist of heterogeneous nodes and edges. Similarity search is a fundamental operation in network systems, which is a basis for various applications such as link prediction and recommendation. This manuscript introduces a temporal similarity measure for heterogeneous networks. The proposed metric is based on metapath strategy and considers time of the interaction. We provide detailed properties of the proposed temporal similarity measures. Our experimental results on a number of real heterogeneous social networks show that incorporating time in the computation of the similarity significantly improves the performance as compared to the state-of-the-art similarity computation methods. • We propose a temporal similarity measure for heterogeneous information networks. • The proposed metric is based on metapath strategy and considers time of the interaction. • Experiments represent the effectiveness of our measure, in comparison with the state-of- the-art similarity computation methods. [ABSTRACT FROM AUTHOR]

Abstract: Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein–protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a -value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and -value, i.e. the more the -value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties. [ABSTRACT FROM AUTHOR]

Community detection is a way to understand the mesoscale characteristics of networked systems and has received much attention recently. Most existing community detection methods suffer from several problems including; weak stability due to employing a randomness factor, requiring the number of communities before starting the community identification process, and unable to recognize communities of various sizes. To overcome these challenges, in this paper a novel subspace-based core expansion method is proposed for identifying non-overlapping communities. The proposed method consists of three main steps. In the first step, the graph is mapped to a low dimensional space using a linear sparse coding method. The main idea behind the mapping strategy is that each data point within a combination of subspaces can be represented as a linear combination of other points. In the second step, a novel node ranking strategy is developed to calculate the goodness of nodes to be considered in identifying community cores. Finally, a novel label propagation mechanism is proposed to form final communities. Several experiments are performed to evaluate the effectiveness of the proposed method on real and synthetic networks. Obtained results reveal the better performance of the proposed method compared to some baseline and state-of-the-art community detection methods. • A novel subspace-based community detection method called SCE is proposed. • SCE maps the graph into a low dimensional subspace using linear sparse coding. • A novel node ranking strategy is proposed to identify community cores. • A novel label propagation strategy is used to form communities around identified cores. • The results of experiments reveal the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Respondent Driven Sampling (RDS) is a popular network-based method for sampling from hidden population. This method is a type of chain referral (or snowball) sampling in which an estimator is used to infer the proportion of the population with that property. Existing RDS estimators are asymptotically unbiased based on various underlying assumptions. However, these assumptions are often violated in practice, and little attention has been given to violation of one of these assumptions on accurately reporting the degree by all nodes. In this paper, we address the violation of this assumption and propose a new estimator based on fuzzy computing. In particular, the number of an individual’s contacts can be a fuzzy concept. Using fuzzy functions, we transform the reported degrees to fuzzy numbers and estimate the infection prevalence in the hidden population by the proposed estimator. We simulate RDS method under the condition that all assumptions are satisfied except the one for the degree, and then evaluate the proposed estimator in synthetic and real datasets. Our results show that the fuzzy-based estimator can reduce the sampling bias in average 54% as compared to the existing methods. [ABSTRACT FROM AUTHOR]

Discovering community structure is one of the most intensively studied problems in network science. Many real networks are composed of nodes belonging to multiple communities. In this manuscript, a new overlapping community detection algorithm is proposed based on neutrosophic set (NS) theory. The proposed community detection method manages uncertainty arisen from imprecise definition of communities, by handling boundary and outlier nodes. In the first step, the proposed algorithm calculates the dissimilarity index between each pair of nodes in the network. Then, in order to keep the original distance between nodes as much as possible, the network structure is mapped into a low-dimensional space by multidimensional scaling. Finally, the neutrosophic c-means algorithm is employed to find communities in the network. The experimental results show that the proposed algorithm can detect communities on real and artificial datasets effectively and accurately. [ABSTRACT FROM AUTHOR]

Some networked systems can be better modeled by multilayer structure where the individual nodes develop relationships in multiple layers. Multilayer networks with similar nodes across layers are also known as multiplex networks. This manuscript proposes a novel framework for predicting forthcoming or missing links in multiplex networks. The link prediction problem in multiplex networks is how to predict links in one of the layers, taking into account the structural information of other layers. The proposed link prediction framework is based on interlayer similarity and proximity-based features extracted from the layer for which the link prediction is considered. To this end, commonly used proximity-based features such as Adamic–Adar and Jaccard Coefficient are considered. These features that have been originally proposed to predict missing links in monolayer networks, do not require learning, and thus are simple to compute. The proposed method introduces a systematic approach to take into account interlayer similarity for the link prediction purpose. Experimental results on both synthetic and real multiplex networks reveal the effectiveness of the proposed method and show its superior performance than state-of-the-art algorithms proposed for the link prediction problem in multiplex networks. • We propose an interlayer similarities measure for multiplex networks. • We introduce a link prediction framework based on interlayer similarities for multiplex networks. • We examine the Impact of interlayer similarities on link prediction in synthetic and real multiplex networks. [ABSTRACT FROM AUTHOR]