Analysis on topological features of deterministic hierarchical complex network

Two characters, scale-free and hierarchical structure, are shown in lots of real systems all over the world. In this article, firstly, we build up a class of network models with hierarchical structure, denoted by N h (t), and prove it to be scale-free. By computing average degrees, we find these network models are all sparse. Secondly, based on their special topological structure, we research analytically average distance of each model belonging to N h (t) and present an iteration computational method of average distance corresponding to every one in N h (t). Finally, we discuss the affect by changing some tunable parameters to average distance in the set N h (t), and compare our hierarchical network models with previous ones, and show some meaning problems for gaining more universal methods of computing average distance in hierarchical networks.

In the real industrial scenario, the setup times and delivery times are two non-negligible factors, but only few studies have considered the open shop scheduling problem with sequence-dependent setup times and delivery times (OSSP-STDT). In this paper, a mixed integer linear programming model is formulated firstly to accurately solve small-size problems, but it will fail when the size of the problem increases. Then, a complex scheduling network model is developed to characterize OSSP-STDT. After comprehensively considering the local topological features and time attributes in the complex network model, an effective heuristic rule based on complex network is established for solving large-size problems. Finally, an actual warehouse scheduling problem is converted into the aforementioned problem and used as one typical application scenario. Experiments have been conducted and computational results show that compared with exact solutions and meta-heuristics, the proposed algorithm can solve the large-size open shop scheduling problem with sequence-dependent setup times and delivery times more effectively and efficiently.

The graduation development such as employment and graduate school admission of college students are important tasks. However, mining the factors that can affect the development of graduation remains challenging, because the most important factor “course” is not independent and inequality, which are always ignored by previous researchers. Furthermore, traditional structured methods cannot handle the complex relationships between courses, and attention networks cannot distinguish the weights of compulsory and elective courses with different distributions. Therefore, we present a Graph-based Hierarchical Attention Neural Network Model with Elective Course (GHANN-EC) for the prediction of graduation development in this study. Specifically, we use graph embedding that captures the unstructured relationships between courses and hierarchical attention that assigns the importance of the courses to excavating course information that represent students’ independent interests, and can more accurately understand the relationship between graduation development and academic performance. Experimental results on the real-world datasets show that GHANN-EC outperforms the existing popular approach.

Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concentrations by integrating multi-source data and simulating pollutant migration. The model captures nonlinear multivariate temporal patterns through hierarchical transformers, fuses global meteorological and local hydrological features via neural networks, and models runoff topologies with physically constrained graph neural networks. For predicting the concentration changes of pollutants discharged from watersheds, it outperforms baselines like multi-layer perceptrons, recurrent neural networks, and long short-term memory networks, with state-of-the-art performance in root mean square error, mean absolute error, and R2. Ablation studies confirm the essential roles of multi-source data integration and watershed topological modeling in enhancing performance. This method of directly modeling physical processes by leveraging the characteristics of different neural network architectures opens up a new path for addressing the interpretability problem in neural earth system modeling, apart from the process-guided deep learning and differentiable modelling methods.

In this paper, we propose a scalable Label Switched Path (LSP) management architecture for Multiprotocol Label Switching (MPLS) traffic engineering using a hierarchical network model. MPLS introduces the concept of a label hierarchy to support scalability by lessening the complexity of transit routers for the computation of complete routing tables. However, this hierarchical network model creates a critical disadvantage in providing a globally-optimal route because of topology abstraction or aggregation. This paper proposes a hierarchical routing scheme that can provide a globally-optimal route in hierarchical MPLS networks using propagation from the Condensed Subordinate Route Information (CSRI). CSRI is summarized route information among border nodes in the lower layer network and is reflected in the process of the LSP path computation in the higher layer network. We also propose the algorithms for generation of CSRI, for reflection CSRI to higher layer network topology, and for computation of an optimal LSP in a higher layer.

The Kuramoto model in complex networks

In this brief, a hierarchical Bayesian network modeling framework is formulated for large-scale process monitoring and decision making, which includes a basic layer and a functional layer. First, the whole process is decomposed into different units, where local Bayesian networks are constructed, providing monitoring information and decision-making capability for the upper layer. The network structure is determined automatically based on the process data in each local unit of the basic layer. Then, through incorporating the topological structure of the process, a functional Bayesian network is further constructed to infer the information from the basic layer, which can be customized according to user demands, such as fault detection, fault diagnosis, and classification of operating status. The performance of the proposed method is evaluated through a benchmark process.

In this paper we introduce a family of planar, modular and self-similar graphs which has small-world and scale-free properties. The main parameters of this family are comparable to those of networks associated with complex systems, and therefore the graphs are of interest as mathematical models for these systems. As the clustering coefficient of the graphs is zero, this family is an explicit construction that does not match the usual characterization of hierarchical modular networks, namely that vertices have clustering values inversely proportional to their degrees.

We propose a self-supervised cluster-based hierarchical reduced-order modelling methodology to model and analyse the complex dynamics arising from a sequence of bifurcations for a two-dimensional incompressible flow of the fluidic pinball. The hierarchy is guided by a triple decomposition separating a slowly varying base flow, dominant shedding and secondary flow structures. All these flow components are kinematically resolved by a hierarchy of clusters. The transition dynamics between these clusters is described by a directed network, called the cluster-based hierarchical network model (HiCNM). Three consecutive Reynolds number regimes for different dynamics are considered: (i) periodic shedding at $Re=80$ , (ii) quasi-periodic shedding at $Re=105$ and (iii) chaotic shedding at $Re=130$ , involving three unstable fixed points, three limit cycles, two quasi-periodic attractors and a chaotic attractor. The HiCNM enables identification of the dynamics between multiple invariant sets in a self-supervised manner. Both the global trends and the local structures during the transition are well resolved by a moderate number of hierarchical clusters. The proposed HiCNM provides a visual representation of transient and post-transient, multi-frequency, multi-attractor behaviour and may automate the identification and analysis of complex dynamics with multiple scales and multiple invariant sets.

In this article, we consider machine learning algorithms to accurately predict two variables associated with the Q-voter model in complex networks, i.e. (i) the consensus time and (ii) the frequency of opinion changes. Leveraging nine topological measures of the underlying networks, we verify that the clustering coefficient (C) and information centrality emerge as the most important predictors for these outcomes. Notably, the machine learning algorithms demonstrate accuracy across three distinct initialization methods of the Q-voter model, including random selection and the involvement of high- and low-degree agents with positive opinions. By unraveling the intricate interplay between network structure and dynamics, this research sheds light on the underlying mechanisms responsible for polarization effects and other dynamic patterns in social systems. Adopting a holistic approach that comprehends the complexity of network systems, this study offers insights into the intricate dynamics associated with polarization effects and paves the way for investigating the structure and dynamics of complex systems through modern methods of machine learning.

Recently, wireless multimedia sensor networks (WMSNs) have emerged as one of the most important technologies, driven by the development of powerful multimedia device such as CMOS. WMSNs require several factors such as resource constraints, specific QoS, high bandwidth and so on. In this paper, we propose a hierarchical heterogeneous network model based routing protocol for WMSNs. In our proposal, proposed network model is classified into monitoring class, delivery class and aggregation class. Also we define two kinds of the routing path in order to ensure bandwidth and QoS. In simulation results, we illustrate the performance of our proposal.

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

Hierarchies are networks that begin with a top node and always add nodes below other nodes. There are no lateral connections so there is only one path connecting any two nodes. Although typically depicted as tree diagrams, hierarchies can also be represented as embedded rectangles. The semantic network model was initially constructed as a hierarchical network model that proposed reaction times to verify statements required retrieving categories and their associated features from hierarchically stored knowledge. Hierarchies also store intuitive ontologies that may differ from scientific ontologies. The different characteristics of hierarchies, networks, and matrices provide a basis for selecting an appropriate diagram for representing knowledge. One characteristic that should influence the selection is whether there are no paths (matrices), one path (hierarchies), or multiple paths (network) between concepts.

Multiple periodic orbits from Hopf bifurcation in a hierarchical neural network with [formula omitted]-symmetry and delays

Lately, in some regions and seasons in China, urban consumers have paid high in buying fresh agricultural products while farmers get unreasonable income from producing them. To seek the reason for the phenomenon and explore ways to simulate it, this study constructed and implemented a complex network model named the Bi-Level Multi-Local-World (BI-MLW model) with characteristics of an interdependent coupling relationship between its participants. To verify the validity of the model, this study implemented an experimental simulation under Small Decentralized Operation Mode (SDOM) and Large Centralized Operation Mode (LCOM) scenarios using Cucurbita pepo and Cucumber in the Tianjin area of China as sample empirical products. Results indicate that nodes do not increase edges rapidly which reflects that even large firms in agricultural business cannot occupy markets fleetly. Furthermore, under the SDOM scenario the BI-MLW model exposes scale-free features with a small average degree value and low average clustering coefficient, while under the LCOM scenario, the model displays a rising average clustering coefficient and a lowered average path length. Both of which are consistent with the common view in literature and features of reality. Thus, the BI-MLW model specially designed for fresh agricultural products supply chain can improve the descriptive ability than conventional Erdös-Rényi (ER), Barabási-Albert (BA), Bianconi-Barabási (BB) network models. Keywords: fresh agricultural products, supplying process, supply chain, complex network, multi-local-world model DOI: 10.25165/j.ijabe.20221501.6353 Citation: Liu Y Q, Xu S W, Liu J J, Zhuang J Y. Analytical bi-level multi-local-world complex network model on fresh agricultural products supply chain. Int J Agric & Biol Eng, 2022; 15(1): 208–215.