Nodes In Network Research Articles

Stroop task and practice effects demonstrate cognitive dysfunction in long COVID and myalgic encephalomyelitis / chronic fatigue syndrome.

The Stroop task was used to investigate differences in cognitive function between Long COVID (LC), Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and healthy control subjects. Subjects viewed four color words or neutral (XXXX) stimuli with the same (congruent) or different color ink (incongruent). Cognitive conflict was inferred from response times for pairings of prestimuli and subsequent stimuli. Overall effects were assessed by univariate analysis with time courses determined for binned response times. LC and ME/CFS had significantly longer response times than controls indicating cognitive dysfunction. Initial response times were ranked LC > ME > HC, and decreased according to power functions. At the end of the task (900s), times were ranked LC = ME > HC. Response times were significantly slower for stimuli following an incongruent prestimulus. Time series for Stroop effect, facilitation, interference, surprise index and practice power law parameters were generally similar in LC, ME/CFS and HC suggesting comparable patterns for recruitment of cognitive resources. The prestimulus data were analyzed and generated positive Stroop and interference effects that were distinct from stimulus effects. LC and ME/CFS have global slowing of response times that cannot be overcome by practice suggesting impaired communications between network nodes during problem solving. Analysis of matched prestimulus - stimulus effects adds a new dimension for understanding cognitive conflict. Cognitive dysfunction in Long COVID and ME/CFS was demonstrated using the Stroop task which found global slowing of response times and limitations of practice effects.

The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.

Synchronization and chimeras in asymmetrically coupled memristive Tabu learning neuron network

The coupling between neuronal oscillators plays an intriguing role in understanding the dynamics of the biological neurons present in realistic situations. Importantly, when the coupling between these neurons assumes an asymmetric nature, it can lead to profound changes in their overall behavior. In order to explore the impact of asymmetrical coupling on neuron models subjected to magnetic flux induction, we employ a coupled Tabu learning neuron model. Specifically, we illustrate the interplay between flux coupling and asymmetric electrical synapses concerning the control parameters of the proposed system using phase portraits, time series, bifurcation analysis, and Lyapunov spectrum. In particular, we show the dynamics by taking into account asymmetric interactions between neurons, from a simple network of two coupled systems to a network of nodes. Primarily, we demonstrate that two coupled systems exhibit synchronization for a fixed magnitude of control parameter with increasing coupling strength. Furthermore, we discuss the collective dynamics for the distinct network connectivity including regular, small-world and random. For all network connections, an increase in coupling strength facilitates a transition from desynchronization to synchronization via chimera state. We believe that attaining synchronization in Tabu learning neuron can act as a pivotal factor for neuron activity, contributing to the realization of such behavior in the context of numerous cognitive processes.

Optimizing Microservice Deployment in Edge Computing with Large Language Models: Integrating Retrieval Augmented Generation and Chain of Thought Techniques

Large Language Models (LLMs) have demonstrated impressive capabilities in autogenerating code based on natural language instructions provided by humans. We observed that in the microservice models of edge computing, the problem of deployment latency optimization can be transformed into an NP-hard mathematical optimization problem. However, in the real world, deployment strategies at the edge often require immediate updates, while human-engineered code tends to be lagging. To bridge this gap, we innovatively integrated LLMs into the decision-making process for microservice deployment. Initially, we constructed a private Retrieval Augmented Generation (RAG) database containing prior knowledge. Subsequently, we employed meticulously designed step-by-step inductive instructions and used the chain of thought (CoT) technique to enable the LLM to learn, reason, reflect, and regenerate. We decomposed the microservice deployment latency optimization problem into a collection of granular sub-problems (described in natural language), progressively providing instructions to the fine-tuned LLM to generate corresponding code blocks. The generated code blocks underwent integration and consistency assessment. Additionally, we prompted the LLM to generate code without the use of the RAG database for comparative analysis. We executed the aforementioned code and comparison algorithm under identical operational environments and simulation parameters, conducting rigorous result analysis. Our fine-tuned model significantly reduced latencies by 22.8% in handling surges in request flows, 37.8% in managing complex microservice types, and 39.5% in processing increased network nodes compared to traditional algorithms. Moreover, our approach demonstrated marked improvements in latency performance over LLMs not utilizing RAG technology and reinforcement learning algorithms reported in other literature. The use of LLMs also highlights the concept of symmetry, as the symmetrical structure of input-output relationships in microservice deployment models aligns with the LLM’s inherent ability to process and generate balanced and optimized code. Symmetry in this context allows for more efficient resource allocation and reduces redundant operations, further enhancing the model’s effectiveness. We believe that LLMs hold substantial potential in optimizing microservice deployment models.

Higher-order structure based node importance evaluation in directed networks

Evaluating the significance of objects with possible relevant information is a crucial topic in information science. Due to the fact that objects related to each other can often be described using complex networks, this topic also forms a fundamental theme in network science. Most traditional methods for characterizing the importance of nodes in complex networks only utilize the binary relationships between node pairs, neglecting the influence brought by higher-order structures. Considering the specific interaction modes between local nodes in the network, this paper associates the higher-order structural characteristics of the network with the importance of the nodes. It constructs an evaluation framework for the importance of nodes in directed networks based on higher-order structures. Experimental analysis on both artificial data and scientific citation data from the APS dataset has validated the effectiveness of the proposed algorithms. Compared with PageRank and eigenvector centrality, the proposed algorithms demonstrated higher accuracy, revealing the role of higher-order structures in node importance evaluation. Finally, a robustness analysis of several algorithms indicated that the proposed algorithms exhibited good robustness.

Incorporating Fairness Into the Gateway‐Based Risk Mitigation Policy for Hazmat Transport

ABSTRACTIn hazardous material transport on road networks, two conflicting objectives must be addressed simultaneously: minimizing risk and minimizing cost. Risk mitigation policies may yield as a secondary outcome uneven flow distribution on the network. This study empowers an existing risk mitigation policy based on gateways (GBP) to improve fairness. According to GBP, each vehicle is obliged to traverse a compulsory node (a gateway) on its minimum cost itinerary from origin to destination. Gateways must be located on a few network nodes and assigned to vehicles to minimize total risk, yielding a bi‐level optimization problem. GBP already proved able to reduce total risk by opening just a few gateways and to a limited detriment of total cost. However, gateways may end up acting as flow concentrators, thus hampering equity. This study aims to bridge the gap between risk mitigation and fairness. To this aim, we generalize the multi‐commodity flow formulation of the problem by imposing a capacity constraint on the nodes, discuss its impact on the model structure, and experimentally investigate whether it is possible to achieve a more equitable risk distribution and how total risk and total cost are affected.

Conditional Community Search Based on Weight Information

Community search aims to identify cohesive subgraphs containing user-given query nodes in social networks. As information technology develops, user demands for community search have become increasingly sophisticated. The searched communities must not only meet the structural cohesiveness requirements but also adhere to some complex search conditions based on Boolean expressions. For example, certain desired nodes should be contained in the communities, while certain undesired nodes cannot exist in the communities, which is called conditional community search. However, existing solutions for conditional community search often introduce some undesired nodes into the identified communities and exhibit relatively low search efficiency. To overcome these drawbacks, therefore, this paper investigates the problem of conditional community search based on weight information. First, we refine the original problem definition of conditional community search and outline the need for an improved algorithm for calculating the weights of the nodes. Then, we explore two novel algorithms for searching conditional communities based on calculated weight information. Finally, we conduct extensive experiments on several real-world datasets to verify the accuracy and efficiency of our proposed searching algorithms.

Optimized multi-head self-attention mechanism for SOH prediction in lithium-ion battery energy storage systems within microgrids

Estimating the State of Health (SOH) of lithium batteries is crucial for the safety and stability of the microgrids. Currently, existing data-driven methods struggle to predict across multiple groups of lithium batteries varying in type, capacity, and degree of degradation, as the prediction process requires the collection of extensive operational data for processing and computation. This paper analyzes the characteristics of lithium battery storage units within the microgrids and proposes a novel prediction method based on an improved attention mechanism. In the operational process, we collected the data under constant current (CC) and constant voltage (CV) charging modes to obtain health indicators with high correlation. We proposed a novel optimized multi-head attention method, designated OM-Attention, which employs a multi-head self-attention-based model to compute multiple sets of lithium batteries within a network node in a simultaneous manner. To validate the effectiveness of the OM-Attention method, we randomly selected four lithium batteries from the NASA public dataset for multiple experiments. Compared to SOH prediction methods, our approach exhibits superior performance with reduced Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Square Error (MSE) to 4.16%, 5.02%, and 0.302×10−4, respectively. Additionally, the R2-score achieved by OM-Attention is 97.7%.

Mechanical Unfolding of Network Nodes Drives the Stress Response of Protein-Based Materials.

Biomaterials synthesized from cross-linked folded proteins have untapped potential for biocompatible, resilient, and responsive implementations, but face challenges due to costly molecular refinement and limited understanding of their mechanical response. Under a stress vector, these materials combine the gel-like response of cross-linked networks with the mechanical unfolding and extension of proteins from well-defined 3D structures to unstructured polypeptides. Yet the nanoscale dynamics governing their viscoelastic response remains poorly understood. This lack of understanding is further exacerbated by the fact that the mechanical stability of protein domains depends not only on their structure, but also on the direction of the force vector. To this end, here we propose a coarse-grained network model based on the physical characteristics of polyproteins and combine it with the mechanical unfolding response of protein domains, obtained from single molecule measurements and steered molecular dynamics simulations, to explain the macroscopic response of protein-based materials to a stress vector. We find that domains are about 10-fold more stable when force is applied along their end-to-end coordinate than along the other tethering geometries that are possible inside the biomaterial. As such, the macroscopic response of protein-based materials is mainly driven by the unfolding of the node-domains and rearrangement of these nodes inside the material. The predictions from our models are then confirmed experimentally using force-clamp rheometry. This model is a critical step toward developing protein-based materials with predictable response and that can enable applications for shape memory and energy storage and dissipation.

Qubit teleportation between a memory-compatible photonic time-bin qubit and a solid-state quantum network node

We report on a quantum interface linking a diamond NV center quantum network node and 795nm photonic time-bin qubits compatible with Thulium and Rubidium quantum memories. The interface makes use of two-stage low-noise quantum frequency conversion and waveform shaping to match temporal and spectral photon profiles. Two-photon quantum interference shows high indistinguishability between converted 795nm photons and the native NV center photons. We use the interface to demonstrate quantum teleportation including real-time feedforward from an unbiased set of 795nm photonic qubit input states to the NV center spin qubit, achieving a teleportation fidelity well above the classical bound. This proof-of-concept experiment shows the feasibility of interconnecting different quantum network hardware.

Identifying influential nodes in social networks from the perspective of attack-defense game.

Influence spread analysis, a critical component of social network studies, focuses on the patterns and effects of information dissemination among interconnected entities. The core of influence spread analysis is to identify influential nodes that involve two distinct aspects: influence maximization (IM) and influence blocking maximization (IBM). However, when IM and IBM occur simultaneously, identifying influential nodes becomes an intricate decision-making challenge. This study addresses identifying influential nodes in social networks through an attack-defense game perspective, where an attacker maximizes influence and a defender minimizes it. We first develop a two-player static zero-sum game model considering resource constraints. Based on the equilibrium strategy of this game, we redefine the concept of influential nodes from various viewpoints. Extensive experiments on synthetic and real-world networks show that, in most cases, the defender preferentially defends critical nodes, while the attacker adopts the decentralized strategy. Only when resources are unevenly matched do both players tend to adopt centralized strategies. This study expands the connotation of influential nodes and provides a novel paradigm for the social network analysis with significant potential applications.

Attribute enhanced random walk for community detection in attributed networks

Traditional community detection methods often rely solely on network topology, potentially overlooking the influence of attributes. However, community detection in attributed networks presents a unique challenge due to the complex interplay between network topology and node attributes. In this paper, we present AERW, a novel approach that integrates both network topology and node attributes for effective community detection. AERW leverages the principle of homophily by aggregating neighbor attributes within triangles to capture higher-order structural information. Then constructing a bipartite graph to effectively integrate topological and attribute information, ensuring that both aspects are equally represented in the community detection process. Next AERW utilizes hierarchical clustering applied to the probability transition matrix derived from the random walk, enabling the identification of community structures without needing prior knowledge of the number of communities. Extensive experimentation across synthetic and real-world networks underscores AERW’s efficacy, establishing it as a superior approach in community detection within attributed networks.

The Grief Networks of Traumatic and Nontraumatic Deaths: Comparing Earthquake- and Illness-Related Losses.

Traumatic death is a risk factor for prolonged grief. Network analysis offers a perspective for understanding traumatic bereavement at a symptom interaction level. This study estimates regularized partial correlation grief networks for bereavement due to earthquake (n = 818) and illness (n = 237), using symptoms from the Inventory of Complicated Grief (ICG) as the nodes. Difficulty in accepting death and loneliness were highly central nodes in both networks. Trust difficulties and longing for the deceased exhibited high centrality in the earthquake-bereaved sample but not in the illness-bereaved sample. The earthquake bereavement network was characterized by high connectivity and a diversity of central symptoms. These results provide insights into understanding the severity of grief after a traumatic loss. Although promoting the integration of the reality of loss and alleviating loneliness is important, it might also be worth considering the role of post-loss interpersonal trust and the distress associated with longing for the deceased for traumatic bereavement.

Identifying influential nodes in complex networks through the k-shell index and neighborhood information

Identifying influential nodes in complex networks through the k-shell index and neighborhood information

Identifying Critical Nodes in Multiplex Complex Networks by using Memetic Algorithms

Identifying Critical Nodes in Multiplex Complex Networks by using Memetic Algorithms

‘It’s fine in practice, but how about in theory?’ State-of-the-art minilateralism between expectations and reality

ABSTRACT This article examines the current state of minilateralism. The overall point is to evaluate the theoretical understanding of minilateralism in light of the empirical development of minilateral groupings, which have grown significantly over the last twenty years. In particular, the article focuses on questions of institutionalisation, the relationship of minilaterals to multilateralism, the growth of minilaterals from US-led alliances, and the type of goods (public or club) minilaterals tend to produce. Section I lays out the framework and context for the argument. Section II surveys the development of minilateralism, comparing and contrasting the emergence and characteristics of ‘legacy’ minilaterals from the mid-twentieth century to contemporary minilaterals. Section III details the empirical record of selected contemporary minilateral cases in order to deepen answers to some of the theoretical questions that have emerged as contemporary minilateralism has become a maturing phenomenon in international affairs. In section IV, the article addresses new issues in minilateral formations, such as inter-regional and non-US-led minilaterals, as well as involvement of the private sector and non-governmental organisations as nodes in minilateral networks. Finally, section V concludes with reflections on minilateralism’s future.

MEA-NAP: A flexible network analysis pipeline for neuronal 2D and 3D organoid multielectrode recordings.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and thus can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches. VIDEO ABSTRACT.

How do network embeddedness and knowledge stock influence collaboration dynamics? Evidence from patents

Science, technology, and innovation are becoming increasingly collaborative, prompting concerted efforts to understand and measure the factors influencing these collaborations. This study aims to explore the driving factors and underlying mechanisms of collaboration dynamics based on patent data. Multilayer longitudinal networks are constructed to scrutinize interactions among organizations as well as the embedding of their knowledge elements in the network fabric. We then analyze the structures and characteristics of collaboration and knowledge networks from global and local perspectives, in which process topological indicators and graphlets are used to feature each organization's collaborative patterns and knowledge stock. Knowledge elements are extracted to present the core concepts of patents, overcoming the limitations of predefined categorizations, such as IPC, when representing technological content and context. By performing a longitudinal analysis using a stochastic actor-oriented model, we integrate network structures, node characteristics, and different dimensions of proximity to model collaboration dynamics and reveal the driving factors behind them. An empirical study in the field of lithography finds that organizations with a larger number of partners or a higher number of annular graphlets in their collaboration networks are less likely to collaborate with others. If an assignee has a more extensive range of knowledge elements and demonstrates a higher capability for knowledge combination, or if its local knowledge network exhibits weaker connectivity, its propensity to seek new collaborators increases. Both cognitive and organizational proximity play important roles in fostering collaboration.

Metropolitan-scale heralded entanglement of solid-state qubits.

A key challenge toward future quantum internet technology is connecting quantum processors at metropolitan scale. Here, we report on heralded entanglement between two independently operated quantum network nodes separated by 10 kilometers. The two nodes hosting diamond spin qubits are linked with a midpoint station via 25 kilometers of deployed optical fiber. We minimize the effects of fiber photon loss by quantum frequency conversion of the qubit-native photons to the telecom L-band and by embedding the link in an extensible phase-stabilized architecture enabling the use of the loss-resilient single-click entangling protocol. By capitalizing on the full heralding capabilities of the network link in combination with real-time feedback logic on the long-lived qubits, we demonstrate the delivery of a predefined entangled state on the nodes irrespective of the heralding detection pattern. Addressing key scaling challenges and being compatible with different qubit systems, our architecture establishes a generic platform for exploring metropolitan-scale quantum networks.

Inhibition of denitrification and enhancement of microbial interactions in the AGS system by high concentrations of quinoline

Quinoline represents a highly toxic and structurally stable nitrogen-containing heterocyclic compound in coking wastewater, posing a potential threat to human beings and the ecological environment. In this study, we investigated the impact of gradually elevating quinoline concentration on pollutant removal efficiency, sludge characteristics, microbial community and their interactions in the aerobic granular sludge (AGS) system. The results demonstrated that AGS was capable of effectively degrading quinoline, with a final removal rate of 90 mg/L quinoline reaching 98.54 ± 0.28%. Notably, the denitrification process was significantly impeded in the presence of 90 mg/L quinoline, with the Phase D effluent displaying a notably high NO3--N concentration of 37.09 ± 21.81 mg/L, primarily attributed to the reduced abundance of norank_f_A4b bacteria. As the quinoline concentration increased, the sludge particle size diminished from 3.46 to 2.60 mm, while the settling performance deteriorated significantly, escalating from 31.29 ± 1.63 mL/g to 62.32 ± 2.87 mL/g. Meanwhile, the protein (PN) content in EPS gradually increased (from 19.87 ± 0.88 mg/g MLVSS to 51.22 ± 3.21 mg/g MLVSS), while the polysaccharide (PS) content fluctuated. Quinoline profoundly modified microbial community composition and structure, with deterministic processes dominating community assembly. Network analysis indicated intensified and complex microbial interactions at 90 mg/L quinoline, characterized by significantly higher positive correlations. In addition, rare taxa (RT) dominated the network nodes, with 74 of 93 key species belonging to RT, highlighting their pivotal roles in sustaining system functions and strengthening microbial connections. This study provides new insights into the effects of quinoline on microbial community structure and interactions in AGS system.