Pairs Of Nodes In Network Research Articles

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion.

The function and structure of brain networks (BN) may undergo changes in patients with end-stage renal disease (ESRD), particularly in those accompanied by mild cognitive impairment (ESRDaMCI). Many existing methods for fusing BN focus on extracting interaction features between pairs of network nodes from each mode and combining them. This approach overlooks the correlation between different modal features during feature extraction and the potentially valuable information that may exist between more than two brain regions. To address this issue, we propose a model using a multi-head self-attention mechanism to fuse brain functional networks, white matter structural networks, and gray matter structural networks, which results in the construction of brain fusion networks (FBN). Initially, three networks are constructed: the brain function network, the white matter structure network, and the individual-based gray matter structure network. The multi-head self-attention mechanism is then applied to fuse the three types of networks, generating attention weights that are transformed into an optimized model. The optimized model introduces hypergraph popular regular term and L1 norm regular term, leading to the formation of FBN. Finally, FBN is employed in the diagnosis and prediction of ESRDaMCI to evaluate its classification performance and investigate the correlation between discriminative brain regions and cognitive dysfunction. Experimental results demonstrate that the optimal classification accuracy achieved is 92.80%, which is at least 3.63% higher than the accuracy attained using other methods. This outcome confirms the effectiveness of our proposed method. Additionally, the identification of brain regions significantly associated with scores on the Montreal cognitive assessment scale may shed light on the underlying pathogenesis of ESRDaMCI.

The Impact of Higher-Order Interactions on the Synchronization of Hindmarsh–Rose Neuron Maps under Different Coupling Functions

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh–Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as two- and three-body interactions in three homogeneous and two heterogeneous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

On optimally solving sub‐tree scheduling for wireless sensor networks with partial coverage: A branch‐and‐cut algorithm

AbstractGiven a wireless sensor network, we consider the problem to minimize its total energy consumption over consecutive time slots with respect to communication activities. Nonempty and disjoint subsets of nodes are required to be active and connected under a tree topology configuration in the different time slots, and each network node must be active in a unique time slot. Moreover, the power required by the same pair of network nodes to communicate on the associated direct channel may vary in the different time slots. The problem has been recently introduced in the literature under the name Sub‐Tree Scheduling for Wireless Sensor Networks with Partial Coverage. We focus on the exact solution of the problem. We present a branch‐and‐cut (BC) algorithm based on a novel integer linear programming formulation which allows avoiding the introduction of symmetries in the solution space. In particular, the algorithm relies on an efficient and nontypical separation algorithm for known valid inequalities, and on an easy‐to‐implement primal bound heuristic. The effectiveness of the BC algorithm is empirically shown through an extensive experimental analysis involving 300 newly generated benchmark instances with up to 200 network nodes and 8 time slots. Additionally, the experimental results show that the BC algorithm represents a valid computational tool to benchmark the performance of heuristics addressing the problem, and can be used in practice, as an heuristic solver, to tackle problem instances that are not too large.

A novel traceback model for DDoS attacks using modified Floyd-Warshall algorithm

Distributed denial of service, DDoS, attacks are drastically increasing, therefore, they cause serious threats for information networks. This paper proposes, for the first time, the use of a graph theoretic approach to exploit the entropy techniques for detecting and tracing back DDoS attackers. It presents a novel approach to traceback DDoS attacks using modified Floyd-Warshall algorithm, TDA/MFWA. Such model starts by feeding the network adjacency matrix in which the link weights are changed to comply with the network traffic entropy, accordingly the reachability from node to node can be examined. Then we borrowed the idea of enumerating all the intermediate points between every pair of network nodes from Floyd-Warshall algorithm and modified it to find out the victim node(s). The fact that entropy at network nodes is systematically accounted using a modified Floyd-Warshall algorithm contributes to the smartness and dependability of TDA/MFWA.

Increasing empathic concern relates to salience network hyperconnectivity in cognitively healthy older adults with elevated amyloid-β burden

Enhanced emotional empathy, the ability to share others’ affective experiences, can be a feature of Alzheimer’s disease (AD), but whether emotional empathy increases in the preclinical phase of the disease is unknown. We measured emotional empathy over time (range = 0 – 7.3 years, mean = 2.4 years) in 86 older adults during a period in which they were cognitively healthy, functionally normal, and free of dementia symptoms. For each participant, we computed longitudinal trajectories for empathic concern (i.e., an other-oriented form of emotional empathy that promotes prosocial actions) and emotional contagion (i.e., a self-focused form of emotional empathy often accompanied by feelings of distress) from informant ratings of participants’ empathy on the Interpersonal Reactivity Index. Amyloid-β (Aβ) positron emission tomography (PET) scans were used to classify participants as either Aβ positive (Aβ+, n = 23) or negative (Aβ-, n = 63) based on Aβ-PET cortical binding. Participants also underwent structural and task-free functional magnetic resonance imaging approximately two years on average after their last empathy assessment, at which time most participants remained cognitively healthy. Results indicated that empathic concern, but not emotional contagion, increased more over time in Aβ+ participants than in Aβ- participants despite no initial group difference at the first measurement. Higher connectivity between certain salience network node-pairs (i.e., pregenual anterior cingulate cortex and periaqueductal gray) predicted longitudinal increases in empathic concern in the Aβ+ group but not in the Aβ- group. The Aβ+ participants also had higher overall salience network connectivity than Aβ- participants despite no differences in gray matter volume. These results suggest gains in empathic concern may be a very early feature of AD pathophysiology that relates to hyperconnectivity in the salience network, a system that supports emotion generation and interoception. A better understanding of emotional empathy trajectories in the early stages of AD pathophysiology will broaden the lens on preclinical AD changes and help clinicians to identify older adults who should be screened for AD biomarkers.

Network Connectivity Game

We investigate the cost allocation strategy associated with the problem of providing service /communication between all pairs of network nodes. There is a cost associated with each link and the communication between any pair of nodes can be delivered via paths connecting those nodes. The example of a cost efficient solution which could provide service for all node pairs is a (non-rooted) minimum cost spanning tree. The cost of such a solution should be distributed among users who might have conflicting interests. The objective of this paper is to formulate the above cost allocation problem as a cooperative game, to be referred to as a Network Connectivity (NC) game, and develop a stable and efficient cost allocation scheme. The NC game is related to the Minimum Cost Spanning Tree games and to the Shortest Path games. The profound difference is that in those games the service is delivered from some common source node to the rest of the network, while in the NC game there is no source and the service is established through the two-way interaction among all pairs of participating nodes. We formulate Network Connectivity (NC) game and construct an efficient cost allocation algorithm which finds some points in the core of the NC game. Finally, we discuss the Egalitarian Network Cost Allocation (ENCA) rule and demonstrate that it finds an additional core point.

Repulsive inter-layer coupling induces anti-phase synchronization

We present numerical results for the synchronization phenomena in a bilayer network of repulsively coupled 2D lattices of van der Pol oscillators. We consider the cases when the network layers have either different or the same types of intra-layer coupling topology. When the layers are uncoupled, the lattice of van der Pol oscillators with a repulsive interaction typically demonstrates a labyrinth-like pattern, while the lattice with attractively coupled van der Pol oscillators shows a regular spiral wave structure. We reveal for the first time that repulsive inter-layer coupling leads to anti-phase synchronization of spatiotemporal structures for all considered combinations of intra-layer coupling. As a synchronization measure, we use the correlation coefficient between the symmetrical pairs of network nodes, which is always close to -1 in the case of anti-phase synchronization. We also study how the form of synchronous structures depends on the intra-layer coupling strengths when the repulsive inter-layer coupling is varied.

Searching for Errors in Models of Complex Dynamic Systems.

Mathematical modeling is seen as a key step to understand, predict, and control the temporal dynamics of interacting systems in such diverse areas like physics, biology, medicine, and economics. However, for large and complex systems we usually have only partial knowledge about the network, the coupling functions, and the interactions with the environment governing the dynamic behavior. This incomplete knowledge induces structural model errors which can in turn be the cause of erroneous model predictions or misguided interpretations. Uncovering the location of such structural model errors in large networks can be a daunting task for a modeler. Here, we present a data driven method to search for structural model errors and to confine their position in large and complex dynamic networks. We introduce a coherence measure for pairs of network nodes, which indicates, how difficult it is to distinguish these nodes as sources of an error. By clustering network nodes into coherence groups and inferring the cluster inputs we can decide, which cluster is affected by an error. We demonstrate the utility of our method for the C. elegans neural network, for a signal transduction model for UV-B light induced morphogenesis and for synthetic examples.

METHOD OF MANAGING ROUTING OF DATA FLOWS IN HETEROGENEOUS NETWORKS UNDER CONFLICT, UNCERTAINTY AND DISTURBANCE

This paper proposes a method of synthesis of data transmission routes in conflicting heterogeneous self-organized wireless data networks under external and internal influences. In this case, routing is understood as the process of determining in a data transmission network one or a set of routes (pathspaths) , that are optimal within the selected criteria between a given pair or set of network nodes. Thus, a route is a sequence of network nodes and data transmission paths that connect a pair of network nodes for communication. The method is based on a mathematical model of the data network operation in a virtual multidimensional parameter space. The model of operation is based on a graph-model of information interaction of network nodes in the process of data transmission between receptor nodes and acceptors in the transmission of data streams. Based on the problem statement, the price function is formally defined as the value of the virtual distance between the nodes of a heterogeneous data network. In contrast to the Floyd-Warshall and Dijkstra algorithms, the synthesis of the data transmission route takes into account both the load of the computing environment of network nodes and the state of information interaction channels of network nodes, as well as possible changes in network parameters during data transmission. The data transmission network is self-organized, has no dedicated nodes, which ensures its reliability under external and internal influences, conflicts in data transmission, as well as variable topology. The proposed method for synthesizing data transmission routes when controlling the routing of data flows in heterogeneous networks is focused on the possibility of implementing the basic model of open systems interaction within the framework of existing protocols - the OSI model, which is a reference network model for communications and the development of network protocols.

A Multimodal Deep Learning-Based Distributed Network Latency Measurement System

Network latency plays an important role in the server-selection process as well as real-time applications. Depending on the network system size, network latency can be either explicitly measured or predicted. While for small-scale systems explicit delay measurements can be performed between any pair of network nodes, this method is not feasible for large-scale networks due to the tremendous traffic and processing overhead. As a result, networking companies as well as researchers use the estimation methods for round-trip time (RTT) in large-scale networks. In such methods, network latency estimation is based on performing a small set of actual RTT measurements and predicting the rest of latencies among all network nodes. However, they suffer from several drawbacks such as poor performance, long convergence duration, or lack of convergence. In this article, we present a novel method of large-scale network latency estimation using artificial intelligence (AI). Our system uses a multimodal deep learning algorithm for high accuracy and computing speed. The proposed AI-based system is trained and evaluated using the well-known KING data set derived from the measurements of a real large-scale network. Performance evaluations show that our proposed approach significantly outperform existing techniques, achieving the 90th percentile relative error of 0.25 and an average accuracy of 96.1%, and 76.4% of the measurements are within 20% estimation error.

A secure and energy efficient ant routing algorithm

In Mobile Ad hoc Networks (MANETs), setting up an ideal and proficient route linking the conveying bodies is the essential objective of the routing protocols. But any assault during the routing stage may upset the communication, paralyzing the whole network. So, providing security in routing for a protected communication between nodes has become a prime concern. In the present study, we propose a Secure Energy Efficient Ant Routing Algorithm (SEEARA) based on Ant Colony Optimization (ACO) algorithm and cryptographic primitives that exercises on power control and secure routing between a pair of network nodes and increases the performance and longevity of the network. Also, it can be realized during simulation studies that SEEARA shows a better solution in comparison with the previously proposed routing protocols.

Network Simplification and K-Terminal Reliability Evaluation of Sensor-Cloud Systems

The sensor-cloud system (SCS) integrates sensors, sensor networks, and cloud for managing sensors, collecting and processing data, and decision-making based on data processed. Though the SCS has received tremendous attention from both academia and industry because of its numerous exciting applications, it still faces the challenge in reliability. The reliability of an SCS is generally referred to as the ability to perform required functions for a given period of time. This work is focused on the $K$ -terminal reliability of an SCS, which is concerned with the successful communication between all pairs of network nodes belonging to a pre-specified subset $K$ . The increased complexity and scale of real-life SCSs require new efficient techniques to evaluate their $K$ -terminal reliability. In this work we make novel contributions by proposing a network simplification method that can effectively remove all redundant network edges and vertices, leading to a significantly reduced network model for accurate and efficient $K$ -terminal reliability analysis. The method is based on graph decomposition and reconstruction through articulation vertices. Empirical studies show that the proposed simplification method integrated with the binary-decision-diagrams based evaluation algorithm can significantly speed up $K$ -terminal reliability analysis of large real-life SCSs.

Probing neural networks for dynamic switches of communication pathways.

Dynamic communication and routing play important roles in the human brain in order to facilitate flexibility in task solving and thought processes. Here, we present a network perturbation methodology that allows investigating dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. We show that network pathways can have characteristic timescales and thus specific preferences for the phase lag between the regions they connect. Specifically, we identified pairs of network nodes whose connecting paths can either be (1) insensitive to the phase relationship between the node pair, (2) turned on and off via changes in the phase relationship between the node pair, or (3) switched between via changes in the phase relationship between the node pair. Regarding the latter, we found that 33% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This reveals a potential mechanistic role that phase offsets and coupling delays might play for the dynamic information routing via communication pathways in the brain.

Speeding Up the Gomory-Hu Parallel Cut Tree Algorithm with Efficient Graph Contractions

A cut tree is a combinatorial structure that represents the edge-connectivity between all pairs of nodes of an undirected graph. Cut trees have multiple applications in dependability, as they represent how much it takes to disconnect every pair of network nodes. They have been used for solving connectivity problems, routing, and in the analysis of complex networks, among several other applications. This work presents a parallel version of the classical Gomory-Hu cut tree algorithm. The algorithm is heavily based on tasks that compute the minimum cut on contracted graphs. The main contribution is an efficient strategy to compute the contracted graphs, that allows processes to take advantage of previously contracted graph instances, instead of always computing all contractions from the original input graph. The proposed algorithm was implemented using MPI and experimental results are presented for several families of graphs and show significant performance gains.

Nonequilibrium dynamics of isostatic spring networks.

Marginally stable systems exhibit rich critical mechanical behavior. Such isostatic assemblies can be actively driven, but it is unclear how their critical nature affects their nonequilibrium dynamics. Here, we study the influence of isostaticity on the nonequilibrium dynamics of active spring networks. In our model, heterogeneously distributed white or colored, motorlike noise drives the system into a nonequilibrium steady state. We quantify the nonequilibrium dynamics of pairs of network nodes by the characteristic cycling frequency ω-an experimentally accessible measure of the circulation of the associated phase space currents. The distribution of these cycling frequencies exhibits critical scaling, which we approximately capture by a mean-field theory. Finally, we show that the scaling behavior of ω with distance is controlled by a diverging length scale. Overall, we provide a theoretical approach to elucidate the role of marginality in active disordered systems.

Network Architecture Underlying Basal Autonomic Outflow: Evidence from Frontotemporal Dementia.

The salience network is a distributed neural system that maintains homeostasis by regulating autonomic nervous system activity and social-emotional function. Here we examined how within-network connectivity relates to individual differences in human (including males and females) baseline parasympathetic and sympathetic nervous activity. We measured resting autonomic nervous system physiology in 24 healthy controls and 23 patients with behavioral variant frontotemporal dementia (bvFTD), a neurodegenerative disease characterized by baseline autonomic deficits. Participants also underwent structural and task-free fMRI. First, we used voxel-based morphometry to determine whether salience network atrophy was associated with lower baseline respiratory sinus arrhythmia (a parasympathetic measure) and skin conductance level (a sympathetic measure) in bvFTD. Next, we examined whether functional connectivity deficits in 21 autonomic-relevant, salience network node-pairs related to baseline autonomic dysfunction. Lower baseline respiratory sinus arrhythmia was associated with smaller volume in left ventral anterior insula (vAI), weaker connectivity between bilateral vAI and bilateral anterior cingulate cortex (ACC), and stronger connectivity between bilateral ACC and bilateral hypothalamus/amygdala. Lower baseline skin conductance level, in contrast, was associated with smaller volume in inferior temporal gyrus, dorsal mid-insula, and hypothalamus; weaker connectivity between bilateral ACC and right hypothalamus/amygdala; and stronger connectivity between bilateral dorsal anterior insula and periaqueductal gray. Our results suggest that baseline parasympathetic and sympathetic tone depends on the integrity of lateralized salience network hubs (left vAI for parasympathetic and right hypothalamus/amygdala for sympathetic) and highly calibrated ipsilateral and contralateral network connections. In bvFTD, deficits in this system may underlie resting parasympathetic and sympathetic disruption.SIGNIFICANCE STATEMENT The salience network maintains homeostasis and regulates autonomic nervous system activity. Whether within-network connectivity patterns underlie individual differences in resting parasympathetic and sympathetic nervous system activity, however, is not well understood. We measured baseline autonomic nervous system activity in healthy controls and patients with behavioral variant frontotemporal dementia, a neurodegenerative disease characterized by resting autonomic deficits, and probed how salience network dysfunction relates to diminished parasympathetic and sympathetic outflow. Our results indicate that baseline parasympathetic and sympathetic tone are the product of complex, opposing intranetwork nodal interactions and depend on the integrity of highly tuned, lateralized salience network hubs (i.e., left ventral anterior insula for parasympathetic activity and right hypothalamus/amygdala for sympathetic activity).

Hub recognition for brain functional networks by using multiple-feature combination

Hubs in complex networks can greatly influence the integration of network functions, and recognition of hubs helps to better understand the interaction between pairs of network nodes. This paper proposes a new hub recognition method with multiple-feature combination for the brain functional networks constructed by resting-state functional Magnetic Resonance Imaging (fMRI). Three single-feature methods, including degree centrality, betweenness centrality and closeness centrality, are used to calculate hubs of the brain functional network separately. For reordering the nodes, a composite equation is constructed based on the three recognition parameters. Network vulnerability and average shortest path length are used to evaluate the importance of the hubs recognized by above four methods. Experimental result demonstrates that, the hubs recognized by multiple-feature combination have more significant differences from ordinary nodes than those by single-feature methods, and they have an important impact on the global efficiency of brain functional networks.

Underwater Delay-Tolerant Routing via Probabilistic Spraying

We propose underwater delay-tolerant routing via probabilistic spraying (UDTN-Prob), a routing protocol for underwater delay-tolerant networks based on the store-and-forward paradigm. Our protocol exploits limited statistical knowledge of the time between two subsequent contacts between pairs of network nodes in order to filter the packets injected into the network, so that only those with a sufficiently high chance of being delivered to their intended destination within a given deadline are actually transmitted. In addition, the foreseen duration of a contact is estimated via a preliminary packet exchange, so that the nodes get a fair share of the contact time to exchange their own data. The transmission is protected against channel-induced packet losses via an automatic repeat query scheme modified to adapt itself to typical underwater transmission times and to the variation of round-trip times induced by node mobility. We simulate the protocol using the DESERT Underwater libraries, that make it possible to accurately reproduce the nodes’ behavior and mobility patterns. Our results show that the proposed protocol achieves significantly better performance than spray-and-wait, which is currently the most typical choice among store-and-forward protocols. Moreover, we show that a two-hop statistical knowledge of the node contact process yields marginally higher utility with respect to a simpler one-hop knowledge, which is also much easier to collect or estimate.

Network inference and community detection, based on covariance matrices, correlations, and test statistics from arbitrary distributions

ABSTRACTIn this article we propose methodology for inference of binary-valued adjacency matrices from various measures of the strength of association between pairs of network nodes, or more generally pairs of variables. This strength of association can be quantified by sample covariance and correlation matrices, and more generally by test-statistics and hypothesis test p-values from arbitrary distributions. Community detection methods such as block modeling typically require binary-valued adjacency matrices as a starting point. Hence, a main motivation for the methodology we propose is to obtain binary-valued adjacency matrices from such pairwise measures of strength of association between variables. The proposed methodology is applicable to large high-dimensional data sets and is based on computationally efficient algorithms. We illustrate its utility in a range of contexts and data sets.

Community detection in the textile-related trade network using a biased estimation of distribution algorithm

It is very important to discovery potential customers or groups for the traditional textile enterprises to enhance its’ market competitiveness. Aiming to discover the implicit characters of the textile-related trade system, A biased estimation of distribution algorithm was proposed to detect the community structure in this paper. This algorithm combined a biased search and a simulated annealing selections strategy which were used to improve both the convergence speed and the accuracy of the EDAs to discovery the communities structure for complex system by maximizing the modularity density. The biased search is efficient by taking into account an asymmetric similarity between any pairs of nodes in network according to the different characteristics and local environment of nodes. The proposed algorithm was applied to detect the community structure for a textile-related trade network with the scale-free character extracted from a set of textile companies by uniquely leveraging each node with economic behavior, and the result show that the algorithm is efficient and competent