Consensus In Networks Research Articles

Analysis of the consensus of double-layer chain networks

The multi-layer network topology structures directly affect the robustness of network consensus. The different positions of edges between layers will lead to a great difference in the consensus of double-layer chain networks. Finding the optimal positions of edges for consensus can help to design the network topology structures with optimal robustness. In this paper, we first derive the coherence of double-layer chain networks with one and two connected edges between layers by graph theory. Secondly, the optimal and worst connection edges positions of the two types of networks are simulated. When there is one edge between layers, the optimal edge connection position is found at 1/2 of each chain, and the worst edge connection position is found at the end node of the chain. When there are two edges between layers, the optimal edges connection positions are located at 1/5 and 4/5 of each chain respectively, and the worst edges connection positions are located at the end node of the chain and its neighbor node. Furthermore, we find that the optimal edge connection positions are closely related to the number of single-layer network nodes, and obtain their specific rules.

Asymptotical set stabilization of large-scale logical networks with stochastic delays and the application in finite-field networks

In this paper, asymptotical set stabilization (ASS) of k-valued logical networks (KVLNs) with stochastic delays is studied by designing network-structure-based (NS-based) distributed pinning controllers (DPCs). Firstly, based on the NS-based graph method, k-valued delayed logical networks (KVDLNs) are converted into KVLNs with virtual vertices to handle the effect of stochastic delays. In addition, KVLNs with virtual vertices are transformed into the corresponding algebraic form by the semi-tensor product (STP) method. Based on the system vertices classification technique and the concept of feedback arc set (FAS), the pinning nodes set (PNS) is determined. The mode-independent and mode-dependent DPCs are designed to realize ASS of KVDLNs based on the determined PNS. Next, the obtained theoretical results are further applied to the consensus of finite-field networks (FFNs) with stochastic delays. Finally, two examples are provided to validate the theoretical results of this paper.

Synchronous t-resilient consensus in arbitrary graphs

We study the number of rounds needed to solve consensus in a synchronous network G where at most t nodes may fail by crashing. This problem has been thoroughly studied when G is a complete graph, but very little is known when G is arbitrary. We define a notion of radius(G,t), that extends the standard graph theoretical notion of radius, for considering all the ways in which t nodes may crash, and we present an algorithm that solves consensus in radius(G,t) rounds. Then we derive a lower bound showing that, among oblivious algorithms, our algorithm is optimal for a large family of graphs including all vertex-transitive graphs.

Hybrid Consensus Mechanism : Achieving Efficient and Secure Consensus in Blockchain Networks

Hybrid Consensus Mechanism : Achieving Efficient and Secure Consensus in Blockchain Networks

On fast queue consensus of discrete-time second-order multi-agent networks over directed topologies

Queue consensus, which aims to drive a set of agents to form a prescribed queue while moving with a common reference velocity, has attracted much attention due to its wide applications. This paper investigates the problem of fast queue consensus for a class of directed multi-agent networks with discrete-time second-order agent dynamics. First, a discrete-time consensus-based control protocol is constructed, and the necessary and sufficient condition to achieve queue consensus is presented. Then, the explicit formulas for the control parameters to achieve the fastest convergence are derived. It is found that the convergence rate of the reference velocity dominates the convergence performance of the overall network. To further accelerate the convergence rate, the agents' node memory is introduced to redesign the update rule of the reference velocity. Furthermore, the analytic formulas of the improved optimal convergence rate and control parameters are established. Finally, numerical examples are given to validate the effectiveness of the proposed theoretical results.

SpiderLearner: An ensemble approach to Gaussian graphical model estimation.

Gaussian graphical models (GGMs) are a popular form of network model in which nodes represent features in multivariate normal data and edges reflect conditional dependencies between these features. GGM estimation is an active area of research. Currently available tools for GGM estimation require investigators to make several choices regarding algorithms, scoring criteria, and tuning parameters. An estimated GGM may be highly sensitive to these choices, and the accuracy of each method can vary based on structural characteristics of the network such as topology, degree distribution, and density. Because these characteristics are a priori unknown, it is not straightforward to establish universal guidelines for choosing a GGM estimation method. We address this problem by introducing SpiderLearner, an ensemble method that constructs a consensus network from multiple estimated GGMs. Given a set of candidate methods, SpiderLearner estimates the optimal convex combination of results from each method using a likelihood-based loss function. -fold cross-validation is applied in this process, reducing the risk of overfitting. In simulations, SpiderLearner performs better than or comparably to the best candidate methods according to a variety of metrics, including relative Frobenius norm and out-of-sample likelihood. We apply SpiderLearner to publicly available ovarian cancer gene expression data including 2013 participants from 13 diverse studies, demonstrating our tool's potential to identify biomarkers of complex disease. SpiderLearner is implemented as flexible, extensible, open-source code in the R package ensembleGGM at https://github.com/katehoffshutta/ensembleGGM.

Decentralized Federated Learning With Markov Chain Based Consensus for Industrial IoT Networks

Federated learning (FL) provides a novel framework to collaboratively train a shared model in a distribution fashion by virtue of a central server. However, FL is inappropriate for a serverless scenario and also suffers from some major drawbacks in Industrial Internet of Things (IIoT) networks, such as unresilience to network failures and communication bottleneck effect. In this article, we propose a novel decentralized federated learning (DFL) approach for IIoT devices to achieve model consensus by exchanging model parameters only with their neighbors rather than a central server. We firstly formulate the problem of model consensus in DFL as a fastest mixing Markov chain problem and then optimize the consensus matrix to improve the convergence rate. Meanwhile, a practical medium access control protocol with time slotted channel hopping is taken into account to implement the proposed approach. Furthermore, we also propose an accumulated update compression method to alleviate communication cost. Finally, extensive simulation results demonstrate that the proposed approach improves accuracy and reduces communication cost especially under the nonindependent identically distribution data distribution.

Privacy-Preserving Dynamic Average Consensus via Random Number Perturbation

This brief focuses on the study of privacy preservation of dynamic average consensus (DAC) in multi-agent networks. A privacy-preserving DAC (PP-DAC) algorithm is proposed based on a carefully designed random number perturbation mechanism. The PP-DAC algorithm is able to protect agents from the leakage of sensitive information without compromising their tracking accuracy. Furthermore, the privacy analysis for different scenarios is given to show that the PP-DAC algorithm works well unless all neighbors of the target agent collude with each other to attack this agent. Also, some numerical simulations are given to illustrate the validity of the proposed algorithm.

Resilient Event-/Self-Triggering Leader-Following Consensus Control of Multiagent Systems Against DoS Attacks

This paper focuses on the problem of resilient leader-following consensus for multi-agent systems (MAS) against denial-of-service (DoS) attacks. Two update strategies based on event/self-triggering are proposed for the control protocols to handle the leader-following consensus in unreliable shared networks. We first propose a dynamic event-triggered communication (DETC) scheme to mitigate unnecessary information transfer through energy-limited and vulnerable networks. Continuous communication between agents can be avoided. DoS attacks are supposed to be aperiodic and asynchronous at different edges. The concept of valid DoS attack interval is introduced, and the frequency and duration of attacks are analyzed. The leader-following consensus can be achieved when there exist DoS attacks using the dynamic event-based control strategy. Then to avoid continuous event detection and save computation resources, a self-triggered communication (STC) function is developed. The next triggering moment is predetermined with the latest received state information. And the Zeno behavior is eliminated for the feasibility of the event/self-triggering scheme. Finally, a simulation is provided to verify the effectiveness of the proposed update strategies and control protocols.

Adaptive Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization.

Weakly-supervised temporal action localization (W-TAL) aims to classify and localize all action instances in untrimmed videos under only video-level supervision. Without frame-level annotations, it is challenging for W-TAL methods to clearly distinguish actions and background, which severely degrades the action boundary localization and action proposal scoring. In this paper, we present an adaptive two-stream consensus network (A-TSCN) to address this problem. Our A-TSCN features an iterative refinement training scheme: a frame-level pseudo ground truth is generated and iteratively updated from a late-fusion activation sequence, and used to provide frame-level supervision for improved model training. Besides, we introduce an adaptive attention normalization loss, which adaptively selects action and background snippets according to video attention distribution. By differentiating the attention values of the selected action snippets and background snippets, it forces the predicted attention to act as a binary selection and promotes the precise localization of action boundaries. Furthermore, we propose a video-level and a snippet-level uncertainty estimator, and they can mitigate the adverse effect caused by learning from noisy pseudo ground truth. Experiments conducted on the THUMOS14, ActivityNet v1.2, ActivityNet v1.3, and HACS datasets show that our A-TSCN outperforms current state-of-the-art methods, and even achieves comparable performance with several fully-supervised methods.

Consensus in fuzzy opinion networks

This paper developed the dynamics of opinion network where a node interacts with only one node in each step and these nodes will not exchange their opinions until the difference of their opinions is below a tolerance threshold. Every node is a Gaussian fuzzy set with the center representing an opinion itself and a standard deviation characterizing an uncertainty about the opinion. The fuzzy opinion network with different uncertainties’ levels of nodes was investigated to show how opinions and their uncertainties propagate and evolve for reaching a consensus in the network. The theoretical and numerical analyses were used to assess the conditions where a consensus can be reached in the fuzzy opinion network.

Multi-transcriptomics reveals RLMF axis-mediated signaling molecules associated with bovine feed efficiency.

The regulatory axis plays a vital role in interpreting the information exchange and interactions among mammal organs. In this study on feed efficiency, it was hypothesized that a rumen-liver-muscle-fat (RLMF) regulatory axis exists and scrutinized the flow of energy along the RLMF axis employing consensus network analysis from a spatial transcriptomic standpoint. Based on enrichment analysis and protein-protein interaction analysis of the consensus network and tissue-specific genes, it was discovered that carbohydrate metabolism, energy metabolism, immune and inflammatory responses were likely to be the biological processes that contribute most to feed efficiency variation on the RLMF regulatory axis. In addition, clusters of genes related to the electron respiratory chain, including ND (2,3,4,4L,5,6), NDUF (A13, A7, S6, B3, B6), COX (1,3), CYTB, UQCR11, ATP (6,8), clusters of genes related to fatty acid metabolism including APO (A1, A2, A4, B, C3), ALB, FG (A, G), as well as clusters of the ribosomal-related gene including RPL (8,18A,18,15,13, P1), the RPS (23,27A,3A,4X), and the PSM (A1-A7, B6, C1, C3, D2-D4, D8 D9, E1) could be the primary effector genes responsible for feed efficiency variation. The findings demonstrate that high feed efficiency cattle, through the synergistic action of the regulatory axis RLMF, may improve the efficiency of biological processes (carbohydrate metabolism, protein ubiquitination, and energy metabolism). Meanwhile, high feed efficiency cattle might enhance the ability to respond to immunity and inflammation, allowing nutrients to be efficiently distributed across these organs associated with digestion and absorption, energy-producing, and energy-storing organs. Elucidating the distribution of nutrients on the RLMF regulatory axis could facilitate an understanding of feed efficiency variation and achieve the study on its molecular regulation.

Matrix-scaled resilient consensus of discrete-time and continuous-time networks

This paper studies the matrix-scaled resilient consensus problems over multi-agent networks as occurring in computer science and distributed control. Unlike existing works on consensus problems, where the states of agents converge to a common value or reach some prescribed proportions, we take a more general matrix-scaled approach to accommodate the interdependence of multi-dimensional states. We develop a unified analytical framework to deal with matrix-scaled resilient consensus of discrete-time and continuous-time dynamical agents, where the underlying communication network is modeled as a generic directed time-dependent random graph. We propose new distributed protocols to guarantee the matrix-scaled consensus of cooperative agents in the network in the presence of Byzantine agents, who have full knowledge of the system and pose a severe security threat to the collective consensus objective. The cooperative agents feature multiple input and multiple output, and the number and identities of Byzantine agents are not available to the cooperative ones. Our mathematical approach capitalizes on matrix analysis, control theory, graph theory, and martingale convergence. Some numerical examples are presented to demonstrate the effectiveness of our theoretical results.

Resilient consensus of high-order networks against collusive attacks

This paper investigates the resilient consensus problem of high-order networks under malicious attacks. The main difficulty lies in the collusion among attacks against the attack detection and isolation. To overcome such difficulty, a novel attack isolation strategy is developed, based on which, a control algorithm is provided to achieve resilient consensus. A new notion of (r,s)-isolability is introduced to present sufficient conditions for attack isolation. Finally, the effectiveness of the proposed approach is demonstrated by simulation studies.

Bureaucratic Challenges in the Implementation Network of Food Diversification Policy in Indonesia

Network governance is an approach that can be used to overcome wicked problems and eliminate the main challenges of the bureaucracy, in the form of limited resources and strict regulations, in the delivery of public services and policies that involve multistakeholders. This study aims to explain the effectiveness of stakeholder interactions in the Food Security Council as a network governance mechanism. This study uses a qualitative design and a case study strategy to explain the challenges of bureaucracy in this policy. The data processing technique used is data reduction through data categorization and classification. Then, the pattern matching analysis technique is the theoretical pattern of the effectiveness of network governance. The results showed that the implementation of the food diversification policy was not yet effective. It can be showed that they do not try to build a collaborative food diversification program as a solution program for food problems. This is due to the lack of understanding of this mechanism, the lack of trust among them, many members and varying interests, unclear consensus network goals, and this mechanism is still weak in coordinating and facilitating interdependent action.
 Keywords: Food diversification; policy; regulations; public services

Measurement-Induced Boolean Dynamics for Open Quantum Networks

In this article, we study the recursion corresponding to the measurement outcomes for open quantum networks under sequential measurements. Open quantum networks are networked quantum subsystems (e.g., qubits) with the state evolutions described by a continuous Lindblad master equation. When measurements are performed sequentially along such continuous dynamics, the quantum network states undergo probabilistic jumps and the corresponding measurement outcomes can be described by a vector of probabilistic Boolean variables. The induced recursion of the Boolean vectors forms a probabilistic Boolean network. First of all, we show that the state transition of the induced Boolean network can be explicitly represented through a real version of the master equation. Next, when the open quantum dynamics are relaxing in the sense that they possess a unique equilibrium as a global attractor, structural properties including absorbing states, reducibility, and periodicity for the induced Boolean network are direct consequences of this relaxing property. Particularly, we show that generically, relaxing quantum dynamics leads to irreducible and aperiodic chains for the measurement outcomes. Finally, we show that for quantum consensus networks, which are a type of nonrelaxing open quantum network dynamics, the communication classes of the measurement-induced Boolean networks are encoded in the quantum Laplacian of the underlying interaction graph

GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks

Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.

Consensus in Networks of Agents with Cooperative and Antagonistic Interactions

This paper studies the consensus of first-order discrete-time multi-agent systems with fixed and switching topology, and there exists cooperative and antagonistic interactions among agents. A signed graph is used to model the interactions among agents, and some sufficient conditions for consensus are obtained by analyzing the eigenvalues of a Laplacian matrix in the case of fixed topology. The results indicate that having a spanning tree is only a necessary condition for the consensus of multi-agent systems with signed graphs, which is also affected by edge weights. Consensus is further discussed in the case of switching topology, and the results reveal that consensus can be reached if the controller gain and the union graphs among some consecutive time intervals satisfy some conditions. Finally, several simulation examples further confirm the theoretical results.

Network analyses of upper and lower airway transcriptomes identify shared mechanisms among children with recurrent wheezing and school-age asthma.

Predicting which preschool children with recurrent wheezing (RW) will develop school-age asthma (SA) is difficult, highlighting the critical need to clarify the pathogenesis of RW and the mechanistic relationship between RW and SA. Despite shared environmental exposures and genetic determinants, RW and SA are usually studied in isolation. Based on network analysis of nasal and tracheal transcriptomes, we aimed to identify convergent transcriptomic mechanisms in RW and SA. RNA-sequencing data from nasal and tracheal brushing samples were acquired from the Gene Expression Omnibus. Combined with single-cell transcriptome data, cell deconvolution was used to infer the composition of 18 cellular components within the airway. Consensus weighted gene co-expression network analysis was performed to identify consensus modules closely related to both RW and SA. Shared pathways underlying consensus modules between RW and SA were explored by enrichment analysis. Hub genes between RW and SA were identified using machine learning strategies and validated using external datasets and quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the potential value of hub genes in defining RW subsets was determined using nasal and tracheal transcriptome data. Co-expression network analysis revealed similarities in the transcriptional networks of RW and SA in the upper and lower airways. Cell deconvolution analysis revealed an increase in mast cell fraction but decrease in club cell fraction in both RW and SA airways compared to controls. Consensus network analysis identified two consensus modules highly associated with both RW and SA. Enrichment analysis of the two consensus modules indicated that fatty acid metabolism-related pathways were shared key signals between RW and SA. Furthermore, machine learning strategies identified five hub genes, i.e., CST1, CST2, CST4, POSTN, and NRTK2, with the up-regulated hub genes in RW and SA validated using three independent external datasets and qRT-PCR. The gene signatures of the five hub genes could potentially be used to determine type 2 (T2)-high and T2-low subsets in preschoolers with RW. These findings improve our understanding of the molecular pathogenesis of RW and provide a rationale for future exploration of the mechanistic relationship between RW and SA.

NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration.

Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks. We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data. All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8. Supplementary data are available at Bioinformatics online.