Random Node Research Articles

Community Detection Algorithm in Social Networks through Iterative Analysis based on Degree of Nodes

Social networking is the grouping of individuals into specific groups, like small rural communities or a neighborhood subdivision. A fundamental problem in the analysis of social networks is the tracking of communities. A community is often defined as a group of network members with stronger ties to members within the group than to members outside the group. The traditional method for identifying communities in networks is hierarchical clustering. Recently, several works have been done in this community identification using different type of clustering algorithm and connectivity-based scoring function. In this paper Random Head Node Technique and Highest Degree Head Node Techniques are proposed to group the nodes into communities. In these techniques best set of centroids are chosen based on the fitness value to cluster the nodes into communities.

Row Hammering Mitigation Using Metal Nanowire in Saddle Fin DRAM

In this article, we investigate a novel technique to minimize row hammer (RH) fail in the saddle fin recessed channel access transistor (S-RCAT) at 1X dynamic random access memory node. We propose a selective introduction of a low work function (WF) metal nanowire (MNW) at the gate metal/gate oxide (GOX) interface to mitigate the RH fail. Using 3-D TCAD simulations, we analyze the mechanism of the RH fail and its mitigation through the use of MNW. The improvement in RH fail is shown due to the MNW and gate-metal work-function-difference induced energy valleys (EVs) between the neighboring S-RCATs. These valleys prevent the diffusion of electrons from aggressor (accessed) to victim (not accessed) cell, which improves RH fail even in the presence of a high concentration of interface states and gate-induced-drain leakage. The introduction of MNW is found not to have any substantial effect on S-RCAT characteristics ( ${V}_{T}$ , ${I}_{ \mathrm{\scriptscriptstyle ON}}$ , or SS). Furthermore, we model the induced EV by considering the WF difference between MNW and gate metal as an effective charge. This model is shown to be in good agreement with the TCAD results. The results obtained in this study demonstrate the potential of using MNW in the gate-stack of S-RCAT for the elimination of RH fail in 1X technology nodes. This technique can also be extended for the use in other device technologies, such as NAND (cell-to-cell interference) and logic transistors.

Adaptive neural output feedback fault tolerant control for a class of uncertain nonlinear systems with intermittent actuator faults

In real applications, actuators of control systems frequently encounter unknown intermittent faults during operation while effectively handing such faults is still a challenge. In this paper, an adaptive neural output feedback fault tolerant control (FTC) scheme based on the command filtered backstepping is developed for a class of uncertain nonlinear systems to address this challenge. In this scheme, a stable nonlinear observer is designed to estimate the system states and neural networks with random hidden nodes are utilized in this observer to approximate unknown functions. A projection algorithm is adopted to estimate system unknown parameters such that the boundedness of parameter estimates is guaranteed. It is proved that the boundedness of all signals in the closed-loop system can be ensured by the proposed modified Lyapunov function. Also the ultimate bound of the tracking error depends on design parameters, adjustable jumping amplitude of Lyapunov function and minimum fault time interval. A truncated L2 bound is established by iterative calculation to illustrate that the transient tracking error performance is determined by design parameters in the controller and observer. Applications on two simulation examples validate the effectiveness of the proposed scheme.

Analytical results for the in-degree and out-degree distributions of directed random networks that grow by node duplication

We present exact analytical results for the degree distribution in a directed network model that grows by node duplication (ND). Such models are useful in the study of the structure and growth dynamics of gene regulatory networks and scientific citation networks. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network and duplicates each outgoing link of the mother node with probability p . In addition, the daughter node forms a directed link to the mother node itself. Thus, the model is referred to as the corded directed-node-duplication (DND) model. The corresponding undirected ND model was studied before and was found to exhibit a power-law degree distribution. We obtain analytical results for the in-degree distribution , and for the out-degree distribution , of the corded DND network at time t. It is found that the in-degrees follow a shifted power-law distribution, so the network is asymptotically scale free. In contrast, the out-degree distribution is a narrow distribution, that converges to a Poisson distribution in the limit of and to a Gaussian distribution in the limit of . Such distinction between a broad in-degree distribution and a narrow out-degree distribution is common in empirical networks such as scientific citation networks. Using these distributions we calculate the mean degree , which converges to in the large network limit, for the whole range of 0 < p < 1. This is in contrast to the corresponding undirected network, which exhibits a phase transition at p = 1/2 such that for p > 1/2 the mean degree diverges in the large network limit. We also present analytical results for the distribution of the number of upstream nodes, , and for the distribution of the number of downstream nodes, , from a random node. We show that the mean values scale logarithmically with the network size. This means that in the large network limit only a diminishing fraction of pairs of nodes are connected by directed paths, unlike the corded undirected ND network that consists of a single connected component. Therefore, the corded DND network is not a small-world network.

Approximation Theory for Connectivity of Ad Hoc Wireless Networks With Node Faults

In this letter, we propose an approximation theory for connectivity of general random geometric graphs (RGGs) with stochastic node faults, which is regarded as an abstract model of ad hoc wireless sensor networks (WSNs) with unreliable nodes. In the model, each node in an RGG is independently at fault and the disconnection probability of a resulting survival network is evaluated. The proposed approximation formula for the probability is asymptotically correct and reveals the phase transition phenomena of connectivity in the model. In addition, we obtain phase transition thresholds of a practical Rayleigh single-input single-output (SISO) model and Rayleigh multiple-input multiple-output (MIMO) model, which shows the effectiveness of the MIMO system against attenuation and random node faults. The numerical results in Rayleigh MIMO model show that the approximation formula well estimates the connection probability even for finite graphs and that it provides useful information for design of WSN immune to node faults.

Characterising the robustness of coupled power-law networks

Many networks exhibit a power-law configuration, where the number of connections each node has follows a power-law distribution, including the Internet, terrorist cells, species relationships and infrastructure. Given the prevalence of power-law networks, studying the effects of disruptions on their performance is of interest. Previous work has investigated the influence of network topology on the effects of random node failures for independent networks. Many networks depend on others to function and thus, exploring the influence of network topology on the effects of failures in interdependent networks is of interest. The present paper extends the previous work to coupled power-law network systems. For a set of randomly generated coupled systems, each containing two networks, we investigate the significant topological factors for different dependency types. Failures in the coupled networks are simulated and the effects on the system performance are analysed by performing a beta regression. The results are consistent across the dependency types, with the most influential topological factors being mean nodal degree and factors relating to the dependency type. The results are also compared with those of the independent networks and their potential relevance to the design of interdependent networks is indicated, for example, their use within an infrastructure setting.

On the Robustness of Complex Systems With Multipartitivity Structures Under Node Attacks

Complex systems in the real world inevitably suffer from unpredictable perturbations, which can trigger system disasters, wreaking significant economical losses. To exploit the robustness of complex systems in the face of disturbances is of great significance. One of the most useful methods for system robustness analysis comes from the field of complex networks characterized by percolation theories. Many percolation theories, therefore, have been developed by researchers to investigate the robustness of diverse complex networks. Nevertheless, extant percolation theories are primarily devised for multilayer or interdependent networks. Little endeavor is dedicated to systems with multipartitivity structures, that is, multipartite networks, which are an indispensable part of complex networks. This paper fills this research gap by theoretically examining the robustness of multipartite networks under random or target node attacks. The generic percolation theory for robustness analysis of multipartite networks is accordingly put forward. To validate the correctness of the proposed percolation theory, we carry out simulations on computer-generated multipartite networks with Poisson degree distributions. The results yielded by the proposed theory coincide well with the simulations. Both theoretical and simulation results suggest that complex systems with multipartitivity structures could be more robust than those with multilayer structures.

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a tradeoff is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

Broadcast and Gossip Stochastic Average Consensus Algorithms in Directed Topologies

We address the problem of a set of agents reaching consensus by computing the average of their initial states. We propose two randomized algorithms over a directed communication graph where either a random node broadcast its value or a randomly selected pair of nodes communicate in a distributed fashion. The proposed algorithms guarantee convergence in three important definitions, namely: almost surely, in expectation, and in the mean-square sense. We show how the parameters of the algorithm can be optimized to improve the rate of convergence and compare its rates of convergence for directed and undirected graphs.

Analytical results for the distribution of shortest path lengths in directed random networks that grow by node duplication

We present exact analytical results for the distribution of shortest path lengths (DSPL) in a directed network model that grows by node duplication. Such models are useful in the study of the structure and growth dynamics of gene regulatory networks and scientific citation networks. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network and duplicates each outgoing link of the mother node with probability $p$. In addition, the daughter node forms a directed link to the mother node itself. Thus, the model is referred to as the corded directed-node-duplication (DND) model. In this network not all pairs of nodes are connected by directed paths, in spite of the fact that the corresponding undirected network consists of a single connected component. More specifically, in the large network limit only a diminishing fraction of pairs of nodes are connected by directed paths. To calculate the DSPL between those pairs of nodes that are connected by directed paths we derive a master equation for the time evolution of the probability $P_t(L=\ell)$, $\ell=1,2,\dots$, where $\ell$ is the length of the shortest directed path. Solving the master equation, we obtain a closed form expression for $P_t(L=\ell)$. It is found that the DSPL at time $t$ consists of a convolution of the initial DSPL $P_0(L=\ell)$, with a Poisson distribution and a sum of Poisson distributions. The mean distance ${\mathbb E}_t[L|L<\infty]$ between pairs of nodes which are connected by directed paths is found to depend logarithmically on the network size $N_t$. However, since in the large network limit the fraction of pairs of nodes that are connected by directed paths is diminishingly small, the corded DND network is not a small-world network, unlike the corresponding undirected network.

Lagrange Operational Matrix Methods to Lane–Emden, Riccati’s and Bessel’s Equations

The current study is presented to develop two approaches and methodologies to find the numerical solution of linear and non-linear initial value problems such as Lane–Emden type equation, Riccati’s equation and Bessel’s equation of order zero based on approximation. The function approximations (scheme-I and scheme-II) are presented to find the numerical solutions of linear and non-linear initial value problems by using Gauss Legendre roots as node points and random node points in the domain [0, 1]. In the scheme-I, the roots of Legendre polynomial are used as node points for Lagrange polynomials and in scheme-II, we have taken random node points in the domain [0, 1] and orthogonalize the resulting Lagrange polynomials using Gram–Schmidt orthogonalization process. Firstly, we have introduced the function approximations by using generating interpolating scaling functions (ISF) and orthonormal Lagrangian basis functions (OLBF) over the space \(L^{2}[0,1]\) then we have constructed the operational matrices of integration and product operational matrices based on newly designed approximations namely ISF and OLBF. These operational matrices convert given linear and non-linear initial value problems into the associated system of algebraic equations. Finally, we have established error bounds (Lemmas 1, 2) of both scheme-I and scheme-II including the function approximations. The efficiency of the proposed schemes has been confirmed with several test examples including numerical stability. So, the schemes are simple, efficient and produces very accurate numerical results in considerably small number of basis functions and hence reduces computational effort.

Gateway sink placement for sensor node grid distribution in lora smart city networks

&lt;span&gt;Low Power Wide Area Network (LPWAN) is a type of wireless communication network designed to allow long range communications at a low bit rate among things (connected objects), such as sensors operated on a battery. It is a new technology that operates in unauthorized spectrum which designed for wireless data communication [1]. It is used in Internet of Thing (IoT) applications and M2M communications. It provides multi-year battery lifetime and is intended for sensors and applications that need to transmit only a few information over long distances a few times per hour from different environments. In order to have an insight of such long range technology, this paper evaluates the performance of LoRa radio links under shadowing effect and realistic smart city utilities node grid distribution. Such environment is synonymous to residential, industrial and modern urban centers. The focus is to include the effect of shadowing on the radio links while attempting to study the optimum sink node numbers and their locations for maximum sensor node connectivity. Results indicate that the usual unrealistic random node distribution does not reflect actual real-life scenario where many of the these sensing nodes follow the utilities infrastructure around the city (e.g., street light posts, water and gas delivery pipes,…etc). The system is evaluated in terms of connectivity and packet loss ratio.&lt;/span&gt;

An enhanced whale optimization algorithm for vehicular communication networks

SummaryIn the field of vehicle communication networks, vehicular ad hoc network (VANET) is established with the help of road side unit (RSU). Each vehicle in the traffic system is managed by RSU with certain mobility factor, but still, there are some issues in the mobility management. In order to avoid such expensive RSUs and to enhance the mobility management of VANET system, the paper proposes a novel algorithm for the process of organizing a cluster structure and cluster head (CH) election suitable for VANETs. The proposed adaptive weighted clustering protocol (AWCP) groups the random nodes, and then the optimal CH is attained by the optimization of network parameters. For the purpose of optimization, an innovative algorithm called the enhanced whale optimization algorithm (EWOA) is introduced. For each vehicle in a trusted clustering model, its movement is analyzed by vehicle network mobility routing protocol with identified speed and position. The distance between the trusted vehicle node and RSU is analyzed by the proposed AWCP‐EWOA model. The results demonstrate the proposed AWCP‐EWOA model outperforms compared with AWCP and AWCP‐Whale Algorithm (WA) protocols in terms of clustering efficiency and its mobility enhancement.

Random Bit Extraction for Secret Key Generation in MANETs

Mobile ad hoc networks are a group of random mobile nodes which does not have a pre-existing base stations or infrastructure. In order to make communications effective over these networks a security scheme is essential. This can be achieved by establishing some shared random bits generated with fundamental secret key generation techniques. This paper presents a secret key generation scheme suitable for networks adapted to dynamic source routing protocol. Investigations were carried out for the possibility of using a system metadata like routing information for randomness extraction. Using routing tables maintained by the nodes as backbone, we are extracting a set of common secret random data for key generation. The secret key generation starts with random source extraction followed by information reconciliation to reduce errors and privacy amplification to increase the confidentiality. The main advantage of this technique is that it utilizes the sources of randomness inherent in the system itself without increasing the computational complexity. It also helps to identify the presence of an adversary in the network. The results of the system are evaluated to analyze the efficiency of the technique in terms of packet loss and packet overhead.

Structural alterations of the brain preceded functional alterations in major depressive disorder patients: Evidence from multimodal connectivity

BackgroundRecent studies showed that major depressive disorder (MDD) has been involved in abnormal functional and structural connections in specific brain regions. However, comprehensive researches on MDD-related alterations in the topological organization of brain functional and structural networks are still limited. MethodsFunctional network (FN) was constructed from resting-state functional MRI temporal series correlations and structural network (SN) was established by Diffusion tensor imaging (DTI) data in 58 MDD patients and 71 healthy controls (HC). The measurements of the network properties were calculated for two networks respectively. Correlations were conducted between altered network parameters and Hamilton depression scale (HAMD) score. Additionally, network resilient analysis were conducted on FN and SN. ResultsThe losses of small-worldness charateristics and the decline of nodal efficiency across FN and SN were found in MDD patients. Based on network-based statistic (NBS) approach, the decreased connections in MDD patients were mainly found in the superior occipital gyrus, superior temporal gyrus for FN and SN, while the increased connections were distributed in putamen, superior frontal gyrus only for SN. Compared with the FN, the SN showed less resilient to targeted or random node failure. Besides, altered edges in NBS and regions with decreased nodal efficiency were negatively associated with HAMD score in MDD patients. LimitationsThe samples size is small and most of the MDD patients take different antidepressant medications. ConclusionsAlterations of SN in the brain of MDD patients preceded that of FN to some extent, and reorganization of the brain network was a mechanism which compensated for functional and structural alterations during disease progression.

Robustness of multipartite networks in face of random node failure

Abstract Complex networks are prevalent in our lives. A complex network usually is composed of many components. Because the components of a network may suffer from random failures or intentional attacks, it is therefore important to study the robustness of networks in face of perturbations. Because real-world complex networks are practically interdependent, therefore many efforts have been made to investigate the robustness of interdependent or multilayer networks. Existing studies indicate that the robustness of multilayer networks displays first order phase transition, while the robustness of single layer networks only displays second order phase transition. Note that a simple form of a multilayer network is a multipartite network. Intuitively, the robustness of multipartite networks will also possess first order phase transition. In this paper we study the robustness of multipartite networks in face of random node failures. Extensive experiments have been carried out to test the robustness of multipartite networks whose degree distributions follow Poisson distribution. Interestingly, we have found that the robustness of multipartite networks displays second-order-like phase transition which is against the intuitive conclusion.

Self-avoiding pruning random walk on signed network

A signed network represents how a set of nodes are connected by two logically contradictory types of links: positive and negative links. In a signed products network, two products can be complementary (purchased together) or substitutable (purchased instead of each other). Such contradictory types of links may play dramatically different roles in the spreading process of information, opinion, behaviour etc. In this work, we propose a self-avoiding pruning (SAP) random walk on a signed network to model e.g. a user’s purchase activity on a signed products network. A SAP walk starts at a random node. At each step, the walker moves to a positive neighbour that is randomly selected, the previously visited node is removed and each of its negative neighbours are removed independently with a pruning probability r. We explored both analytically and numerically how signed network topological features influence the key performance of a SAP walk: the evolution of the pruned network resulted from the node removals, the length of a SAP walk and the visiting probability of each node. These findings in signed network models are further verified in two real-world signed networks. Our findings may inspire the design of recommender systems regarding how recommendations and competitions may influence consumers’ purchases and products’ popularity.

Female sex workers in HIV/AIDS prevention: A social network analysis perspective

Female Sex Workers (FSWs) are a high-risk group that not only are susceptible to, but also may contribute to the spread of, HIV/AIDS. How to effectively intervene the FSW population for HIV/AIDS prevention has been a public concern. In this paper, relied on a set of interview data of FSWs collected from a city in Yunnan Province, China, we first construct a social network based on if FSWs have been acquaint. We find that the network has the characteristics of “small world” with a small average shortest path and a big clustering coefficient. The network also shows a “small community” phenomenon with a low average density. In other words, individuals are close to each other in community, and their external connection is sparse. The degree of the FSW social network obeys an exponential distribution, indicating that few numbers of nodes have a large centrality (i.e. the important individuals). In addition, those nodes that is more passive in HIV/AIDS prevention have a higher level of clustering, but a lower similarity with their neighbors. Finally, we use individuals’ responses to the AIDS KAP questionnaire to predict one’s position (or status) in the social network, as the social network structure is invisible, observable individual responses to the questionnaire is easier to obtain. We then propose an efficient method to choose the nodes based on individuals’ responses to AIDS KAP questionnaire for intervention and network clogging. We found that, compared with a random node selection method, our method is more effective.

Location Region Estimation for Internet of Things: A Distance Distribution-Based Approach

Location region estimation (LRE) is a key issue for many location-based applications in the Internet of Things era. This paper explores the problem of accurate LRE (ALRE) with distance distribution methods. First, in order to capture the uncertainties during the distance ranging process, a disk error model is introduced by modeling the target as a random node inside a disk region. Then, a disk error-based ranging (DEBR) approach is designed and analyzed by proving that the parameter estimation of DEBR is unbiased. Furthermore, an ALRE algorithm is developed through taking into account both DEBR and the classical multilateration method. It is proved that the estimated region obtained by ALRE is tighter than that obtained by the traditional estimation method. In addition, extensive simulations are conducted to verify the unbiased estimation of DEBR and evaluate the performance of ALRE.

A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification

Spectral-spatial feature extraction is of great importance to hyperspectral image (HSI) classification. Different from the traditional feature extraction methods, deep learning models such as convolutional neural network (CNN) can learn the spectral-spatial discriminative feature automatically. However, deep learning models usually need to construct a large and complicated network and the training is time-consuming. To deal with these issues, in this paper, a spectral-spatial domain-specific convolutional deep extreme learning machine (ELM), named S 2 CDELM, is proposed for HSI classification. At first, by using the conception of local receptive filed (LRF), a spectral-spatial convolutional learning module with two branches is constructed for spectral and spatial feature extraction respectively. Specifically, the convolutional learning module is constructed by using random convolutional nodes but without back propagation, in which a spectral branch and a spatial branch are designed respectively. Then the extracted features are concatenated and fed to a fully connected stacked ELM network to further exploit spectral-spatial information for classification. As the convolutional filters and input weights of ELM are randomly generated, the whole framework is compact, simple and fast to construct. Experimental results on popular HSI benchmark data sets demonstrate that S 2 CDELM can provide satisfactory classification performance and a fast learning speed in comparison with several state-of-the-art classifiers.