Consensus In Networks Research Articles

A Global Temporal Networked Consensus Mining Method for Social Product Development

A Global Temporal Networked Consensus Mining Method for Social Product Development

Simulation and Evaluation of Distributed consensus Network for Multi-Agent Systems for Sybil Attacks

Distributed average consensus represents the amount of computing of inputs held by multiple agents that communicate through peer-to-peer networks. Collaboration among operators is essential for any distributed standard consensus protocol as every specialist needs to contribute to different operators, typically the adjacent (neighbouring) operators. Internet-of-Things (IoT) implementation is challenging because of its heterogeneous, massively distributed nature. The challenges of this challenge can be addressed with blockchain-based platforms and technologies. Testing and evaluation platforms are required for Blockchain deployments in IoT. A realistic and configurable network environment is presented in this paper to evaluate consensus algorithms. Many blockchainevaluation platforms do not provide a configurable and realistic network environment or are tied to a specific consensus protocol. With our simulator, practitioners can evaluate how consensus algorithms affect network events in congested or contested scenarios to determine the best consensus algorithm. It is proposed to achieve this task by generalizing consensus methods. The Blockchain simulator employs Discrete event network simulations for increased fidelity and scalability. In addition to evaluating the time, state block rate (%), estimation error, average throughput, and simulation time, we evaluate the performance of the proposed techniques based on the number of peer nodes. A comparison of the average transaction delivery rate with a traditional protocol is shown. The proposed protocol has a higher throughput average than the traditional one.

Emergence mechanisms of group consensus in social networks

Emergence mechanisms of group consensus in social networks

Application of Blockchain Based on Fabric Consensus Network Model in Secure Encryption of Educational Information

Abstract This paper combines blockchain technology with an information encryption algorithm to design a blockchain-based educational information protection model. It focuses on the role of Fabric architecture and blockchain data structure for information security protection. A chaotic cipher is used to encrypt the information, combined with the iteration of nonlinear functions to generate pseudo-random numbers to realize the unpredictability of the information. Using symbolic dynamics to obtain the control parameter or initial value of the chaotic mapping, combining the sequence of symbols to solve for another value, and finally presenting it in the form of ciphertext in the client for the data accessor to query and access the data. The effectiveness and practicality of educational information security encryption are examined by applying the model of constructed information protection. The results show that the response time of the blockchain module changes depending on the information, and the response time is basically maintained between 2.2 seconds and 2.4 seconds, which is in a reasonable range. In educational information security defense, the information protection model is able to effectively defend the network against attacks, with the highest number of attacks against the attack named external, with the highest number close to 50 million.

Global feedback mechanism by explicit and implicit power for group consensus in social network

This paper investigates a global feedback consensus model which determines the importance of decision makers by combining explicit and implicit Power. Explicit power is obtained according to the differences in organizational hierarchy, while implicit power manifests itself by being recognized in social network interaction. Combining these two types of power, an approach is proposed to characterize the binary power of decision makers jointly to identify the key decision units. Then, a global feedback mechanism based on the binary power is investigated. Its innovation is that it searches the adjustors who are most conducive to achieve group consensus in global and implement personalized feedback services to achieve the minimum cost optimization goal. Finally, an illustrative example to verify the validity of the proposed method are reported.

Topological approach and analysis of clustering in consensus networks

We study clustering properties of networks of single-integrator nodes over a directed graph, in which the nodes converge to steady-state values. These values define clustering groups of nodes, which are considered dependent on interaction topology and edge weights. Focusing on the interaction topology of the network, in this paper, we introduce the notion of topological clusters, which are sets of nodes that converge to an identical value due to the topological characteristics of the network, independent of the value of the edge weights. We then investigate properties of topological clusters and present a necessary and sufficient condition for a set of nodes to form a topological cluster. We also provide an algorithm for finding topological clusters, which is validated by an example.

Comparison and design of organizational decision mechanisms

Individuals with bounded rationality may make incorrect decisions regarding innovation projects. Organizational decision mechanisms aim to minimize the impact of individual fallibility and integrate collective wisdom. In recent years, consensus mechanism has posed a challenge to the traditional hierarchy, but its superiority in different decision scenarios is still unknown. We created decision scenarios based on positions (middle manager or grassroots employee) of the proposer of the innovation project, market volatility, and decision timeliness. Simulation results using Agent Based Modeling indicate the following: If the proposer is a middle manager and decision timeliness is unimportant, consensus in low-density networks is preferred; otherwise, hierarchy is preferred. If the proposer is a grassroots employee, the best-performing mechanism, in order, is consensus in low-density networks, consensus in high-density networks, and hierarchy as the discount interest rate increases. Market volatility drives the advance of the two critical points of the discount interest rate. Furthermore, we propose a hybrid organizational decision mechanism that can balance authority and consensus. It combines the high decision accuracy of consensus with the high decision efficiency of hierarchy, addressing the long decision time in consensus and the information asymmetry and information loss in hierarchy. Simulation results show that the proposed hybrid decision mechanism is only slightly inferior to consensus mechanism when market volatility is very low or decision timeliness is unimportant.

Hierarchical Cyclic Pursuit: Algebraic Curves Containing the Laplacian Spectra

The paper addresses the problem of multiagent communication in networks with regular directed ring structure. These can be viewed as hierarchical extensions of the classical cyclic pursuit topology. We show that the spectra of the corresponding Laplacian matrices allow exact localization on the complex plane. Furthermore, we derive a general form of the characteristic polynomial of such matrices, analyze the algebraic curves its roots belong to, and propose a way to obtain their closed-form equations. In combination with frequency domain consensus criteria for high-order SISO linear agents, these curves enable one to analyze the feasibility of consensus in networks with varying number of agents.

Novel Stability Conditions for Nonlinear Monotone Systems and Consensus in Multiagent Networks

We introduce a novel definition of monotonicity, termed “type-K” in honor of Kamke, and study nonlinear type-K monotone dynamical systems possessing the plus-subhomogeneity property, which we call “K-subtopical” systems after Gunawardena and Keane. We show that type-K monotonicity, which is weaker than strong monotonicity, is also equivalent to monotonicity for smooth systems evolving in continuous-time, but not in discrete-time. K-subtopical systems are proved to converge toward equilibrium points, if any exists, generalizing the result of Angeli and Sontag about convergence of topical systems' trajectories toward the unique equilibrium point when strong monotonicity is considered. The theory provides an new methodology to study the consensus problem in nonlinear multi-agent systems (MASs). Necessary and sufficient conditions on the local interaction rule of the agents ensuring the K-subtopicality of MASs are provided, and consensus is proven to be achieved asymptotically by the agents under given connectivity assumptions on directed graphs. Examples in continuous-time and discrete-time corroborate the relevance of our results in different applications

Accelerated Reinforcement Learning via Dynamic Mode Decomposition

This work applies the decomposition principle to discrete-time reinforcement learning to solve the optimal control problems for a network of subsystems. The control design is defined as a linear quadratic regulator graphical problem, where the performance function couples subsystems' dynamics. We first present a model-free discrete-time reinforcement learning algorithm based on online behaviors without using system dynamics. This could become a prohibitively-long learning process for larger networks. To remedy this issue, we develop an efficient model-free reinforcement learning algorithm based on dynamic mode decomposition. This decomposition method reduces the size of the measured data while the dynamic information of the original network is still retained. This algorithm is then implemented online. The proposed methodology is validated using examples of a consensus network and a power system network.

Longitudinal lipidomic profiles during pregnancy and associations with neonatal anthropometry: findings from a multiracial cohort

Maternal lipidomic profiling offers promise for characterizing lipid metabolites during pregnancy, but longitudinal data are limited. This study aimed to examine associations of longitudinal lipidomic profiles during pregnancy with multiple neonatal anthropometry using data from a multiracial cohort. We measured untargeted plasma lipidome profiles among 321 pregnant women from the NICHD Fetal Growth Study-Singletons using plasma samples collected longitudinally during four study visits at gestational weeks (GW) 10-14, 15-26, 23-31, and 33-39, respectively. We evaluated individual lipidomic metabolites at each study visit in association with neonatal anthropometry. We also evaluated the associations longitudinally by constructing lipid networks using weighted correlation network analysis and common networks using consensus network analysis across four visits using linear mixed-effects models with the adjustment of false discover rate. Multiple triglycerides (TG) were positively associated with birth weight (BW), BW Z-score, length and head circumference, while some cholesteryl ester (CE), phosphatidylcholine (PC), sphingomyelines (SM), phosphatidylethanolamines (PE), and lysophosphatidylcholines (LPC 20:3) families were inversely associated with BW, length, abdominal and head circumference at different GWs. Longitudinal trajectories of TG, PC, and glucosylcermides (GlcCer) were associated with BW, and CE (18:2) with BW z-score, length, and sum of skinfolds (SS), while some PC and PE were significantly associated with abdominal and head circumference. Modules of TG at GW 10-14 and 15-26 mainly were associated with BW. At GW 33-39, two networks of LPC (20:3) and of PC, TG, and CE, showed inverse associations with abdominal circumference. Distinct trajectories within two consensus modules with changes in TG, CE, PC, and LPC showed significant differences in BW and length. The results demonstrated that longitudinal changes of TGs during early- and mid-pregnancy and changes of PC, LPC, and CE during late-pregnancy were significantly associated with neonatal anthropometry. National Institute of Child Health and Human Development intramural funding.

Data-Driven Adaptive Iterative Learning Bipartite Consensus for Heterogeneous Nonlinear Cooperation-Antagonism Networks.

Heterogeneous dynamics, strongly nonlinear and nonaffine structures, and cooperation-antagonism networks are considered together in this work, which have been considered as challenging problems in the output consensus of multiagent systems. A heterogeneous linear data model (LDM) is presented to accommodate the nonlinear nonaffine structure of the heterogeneous agent. It also builds an I/O dynamic relationship of the agents along the iteration-dimensional direction to make it possible to learn control experience from previous iterations to improve the transient consensus performance. Then, an adaptive update algorithm is developed for the estimation of the uncertain parameters of the LDM to compensate for the unknown heterogeneous dynamics and model structures. To address the problem of cooperation and antagonism, an adaptive learning consensus protocol is proposed considering two signed graphs, which are structurally balanced and unbalanced, respectively. The learning gain can be regulated using the proposed adaptive updating law to enhance the adaptability to the uncertainties. With rigorous analysis, the bipartite consensus is proven in the case that the graph is structurally balanced, and the convergence of the agent output to zero is also proven in the case that the graph is unbalanced in its structure. The presented bipartite consensus method is data-based without the use of any explicit model information. The theoretical results are demonstrated through simulations.

Unraveling the evolutionary patterns and phylogenomics of coronaviruses: A consensus network approach.

The COVID-19 pandemic emphasizes the significance of studying coronaviruses (CoVs). This study investigates the evolutionary patterns of 350 CoVs using four structural proteins (S, E, M, and N) and introduces a consensus methodology to construct a comprehensive phylogenomic network. Our clustering of CoVs into 4 genera is consistent with the current CoV classification. Additionally, we calculate network centrality measures to identify CoV strains with significant average weighted degree and betweenness centrality values, with a specific focus on RaTG13 in the beta genus and NGA/A116E7/2006 in the gamma genus. We compare the phylogenetics of CoVs using our distance-based approach and the character-based model with IQ-TREE. Both methods yield largely consistent outcomes, indicating the reliability of our consensus approach. However, it is worth mentioning that our consensus method achieves an approximate 5000-fold increase in speed compared to IQ-TREE when analyzing the data set of 350 CoVs. This improved efficiency enhances the feasibility of conducting large-scale phylogenomic studies on CoVs.

Security and dependability analysis of blockchain systems in partially synchronous networks with Byzantine faults

Blockchains enhance trust in decentralized systems through components like distributed consensus, peer-to-peer communication, and trustless computing. Deployed in real networks, they must ensure security and dependability at each level. This paper introduces security and dependability concepts in three key layers: consensus protocols, network infrastructures, and smart contract applications, derived from distributed systems theory and adapted to blockchains. It evaluates these attributes across eight famous blockchains, assuming realistic network deployments and Byzantine fault scenarios to reveal system strengths and weaknesses. The aim is to establish a comprehensive framework for blockchain assessments, providing insight into design choices and potential security issues.

Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach.

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups.

Transmission-Constrained Consensus of Multiagent Networks

This paper studies the consensus problem for multiagent systems with transmission constraints. A novel model of multiagent systems is proposed where the information transmissions between agents are disturbed by irregular distortions or interferences (named transmission constraint functions), and this model is universal which can be applied in many cases, such as interval consensus and discarded consensus. In the transmission-constrained consensus problem, we obtain the necessary and sufficient condition that agents can converge to state consensus. Furthermore, a more general case is studied in which the system reaches an equilibrium. Based on some techniques of stability theory, the existence, uniqueness and stability of the system equilibrium point can be proven, which means the system can reach an asymptotically stable equilibrium. Moreover, the state values of the equilibrium are only decided by the network structure and transmission constraint functions, but not the agents' initial states. Finally, numerical simulations are presented to illustrate the proposed theorems and corollaries.

Data governance for wicked problems: A case from the Thai health system

AbstractThis study examined how data governance practices in multi‐agency organizations manifest while trying to solve a wicked problem. This study adopted the data governance framework to analyze the Working Group that is responsible for opium addiction and treatment projects in Thailand. This framework comprises five decision domains of data: data principles, data quality, metadata, data access, and data lifecycle. The results showed that the Working Group did not have strong data principles among the relevant agencies and faced local obstacles. This led to uncertainty in the other four areas. It demonstrated, however, some effective outcomes by improvising its specific approach, which considers the relationships between various stakeholders in their dynamic adaptations within the overall data governance process. The Working Group practiced localized framework strategies, which consisted of eight approaches: data consensus in the knowledge network, strong structure, information integrity and cultural awareness, flexibility of organizational integration, dynamic adjustment process, effective information sharing, unlocking stagnations, and data governance cycles. All these strategic approaches compensated for the low performance of the current universal data governance framework.

Leader-following consensus of finite-field networks with time-delays

In this paper, we investigate the leader-following consensus problem for high-ordered finite-field networks (FFNs) with time-delays. It is supposed that the dynamics of each agent is described by a linear equation over a finite field Fp, a distributed control is exerted on the followers to track the leader, and the communication delays are also considered. For such time-delayed FFNs, criteria established on system matrices and interaction graphs are obtained for the leader-following consensus under both fixed and switching topologies. Basing on these criteria, the matrix for the distributed control law is designed. At last, an illustrative example is demonstrated.

Graph matching for knowledge graph alignment using edge-coloring propagation

Knowledge graph (KG) is a kind of structured human knowledge of modeling the relationships between real-world entities. High quality KG is of crucial importance for many knowledge-based applications, e.g., question answering, recommender systems, etc. This paper studies the problem of entity alignment in KGs to promote knowledge fusion. Existing methods model the semantic representation of entities by using graph structural information or attribute information of the KG and then align the entities across different domains by calculating the distances between entities’ embeddings. However, these methods only consider the node-to-node similarity in the alignment procedure while the edge-to-edge similarity is ignored. Our research hypothesis is that the graph edge alignment information is critical in entity alignment. We reformulate the knowledge entity alignment as a quadratic assignment problem (QAP) by adding relation alignment under the one-to-one mapping constraint. To solve the notorious QAP in a large-scale heterogeneous graph like KG, we propose a model, dual neighborhood consensus network (DNCN), which approximately decomposes the QAP into two small-scale linear assignment problems, i.e., entity alignment and relation alignment. After that, an edge-coloring propagation method is proposed to refine the coarse entity alignment result using the relation correspondence. Theoretical proof shows that this method can guarantee the isomorphism between local sub-graphs. The performance of DNCN is evaluated using the DBP15K and DWY100K benchmarks. Experimental results show that DNCN achieves the best performance on the DBP15K benchmark, and is computationally efficient. Ablation studies verify the importance of graph edge alignment information.

Distributed state estimation for uncertain nonlinear time-delay AC islanded micro grids

This article deals with the problem of distributed observer design containing time delays for uncertain nonlinear distributed generators systems in alternating current–islanded micro grids. In contrast with the centralized observer with the ability to accumulate full output of the plant, we design a set of distributed observers, each having access to partial output of the plant through a distributed observer network. Each observer obtains partial plant output and communicates with its neighboring observers through consensus network. The design method is based on the Lipschitz condition of uncertain nonlinear terms while having time-delay system in view. Suitable selection of Lyapunov functions is made, and the Jensen inequality is considered in delayed uncertain nonlinear micro grids in an undirected network. Furthermore, via design algorithm, the necessary and sufficient conditions for the design of parameters are determined so as to stabilize the error dynamics. Finally, numerical simulations are provided to consider the effectiveness of the proposed method.