Scale-free Characteristics Research Articles

Type-II Apollonian network: More robust and more efficient Apollonian network

The family of planar graphs is a particularly important family and models many networks including the layout of printed circuits. The widely-known Apollonian packing process has been used as guideline to create the typical Apollonian network with planarity. In this paper, we propose a new principled framework based on the Apollonian packing process to generate model as complex network, and obtain a family of new networks called Type-II Apollonian network At. While our network and the typical Apollonian network are maximal planar, the former turns out to be Hamiltonian and Eulerian, however, the latter is not. Then, we in-depth study some fundamental structural properties on network At, and verify that network At is sparse, has scale-free feature and small-world property, and exhibits disassortative mixing structure. Next, we derive the asymptotic solution of the spanning tree entropy of network At by designing an effective algorithm, which suggests that Type-II Apollonian network is more robust to a random removal of edges than the typical Apollonian network. Additionally, we study trapping problem on network At, and use average trapping time as metric to show that Type-II Apollonian network At has more efficient underlying structure for fast information diffusion than the typical Apollonian network.

Structural Properties on Scale-Free Tree Network with an Ultra-Large Diameter

Scale-free networks are prevalently observed in a great variety of complex systems, which triggers various researches relevant to networked models of such type. In this work, we propose a family of growth tree networks \(\mathcal{T}_{t}\) , which turn out to be scale-free, in an iterative manner. As opposed to most of published tree models with scale-free feature, our tree networks have the power-law exponent \(\gamma=1{ + }\ln 5/\ln 2\) that is obviously larger than \(3\) . At the same time, “small-world” property can not be found particularly because models \(\mathcal{T}_{t}\) have an ultra-large diameter \(D_{t}\) (i.e., \(D_{t}\sim|\mathcal{T}_{t}|^{\ln 3/\ln 5}\) ) and a greater average shortest path length \(\langle\mathcal{W}_{t}\rangle\) (namely, \(\langle\mathcal{W}_{t}\rangle\sim|\mathcal{T}_{t}|^{\ln 3/\ln 5}\) ) where \(|\mathcal{T}_{t}|\) represents vertex number. Next, we determine Pearson correlation coefficient and verify that networks \(\mathcal{T}_{t}\) display disassortative mixing structure. In addition, we study random walks on tree networks \(\mathcal{T}_{t}\) and derive exact solution to mean hitting time \(\langle\mathcal{H}_{t}\rangle\) . The results suggest that the analytic formula for quantity \(\langle\mathcal{H}_{t}\rangle\) as a function of vertex number \(|\mathcal{T}_{t}|\) shows a power-law form, i.e., \(\langle\mathcal{H}_{t}\rangle\sim|\mathcal{T}_{t}|^{1+\ln 3/\ln 5}\) . Accordingly, we execute extensive experimental simulations, and demonstrate that empirical analysis is in strong agreement with theoretical results. Lastly, we provide a guide to extend the proposed iterative manner in order to generate more general scale-free tree networks with large diameter.

Analysis of Topological Properties and Robustness of Urban Public Transport Networks

With the acceleration of urbanization, public transport networks are an important part of urban transport systems, and their robustness is critical for city operation. The objective of this study is to analyze the topological properties and robustness of an urban public transport network (UPTN) with a view to enhancing the sustainability of urbanization. In order to present the topological structure of the UPTN, the L-Space complex network modeling method is used to construct a model. Topological characteristics of the network are calculated. Based on single evaluation indices of station significance, a comprehensive evaluation index is proposed as the basis for selecting critical stations. The UPTN cascading failure model is established. Using the proportion of the maximum connected subgraph as the evaluation index, the robustness of the UPTN is analyzed using different station significance indices and deliberate attack strategies. The public transport network of Xuzhou city is selected for instance analysis. The results show that the UPTN in Xuzhou city has small-world effects and scale-free characteristics. Although the network has poor connectivity, it is a convenient means to travel for residents with many independent communities. The network’s dynamic robustness is demonstrably inferior to its static robustness due to the prevalence of cascading failure phenomena. Specifically, the failure of important stations has a wider impact on the network performance. Improving their load capacity and distributing the routes via them will help bolster the network resistance against contingencies. This study provides a scientific basis and strategic recommendations for urban planners and public transport managers to achieve a more sustainable public transport system.

Evolution of green travel behaviour on dynamic social networks

Encouraging residents’ green travel behaviour can reduce carbon emissions. However, existing research focuses more on the individual level than on green travel in the context of group interactions. This study aims to connect individual- and group-level insights by integrating the theory of planned behaviour (TPB) and the norm activation model (NAM). Based on empirical data from a questionnaire survey and web crawler, a simulation of the evolution of green travel behaviour interactions in social networks was conducted. The results reveal that the theory of planned behaviour and the norm activation model (TPB −NAM) can explain and predict green travel behaviour. With individuals’ interactions, social networks present scale-free characteristics, and the state of green travel behaviour tends to be stable. Lowering the opinion-bounded confidence of agents in social networks could promote green travel behaviour. This study extends the literature on the theory of green travel behaviour in terms of model integration and interactive decision-making between individuals and groups. The results support the promotion of residents’ green travel behaviour.

Rényi entropy-complexity causality space: a novel neurocomputational tool for detecting scale-free features in EEG/iEEG data.

Scale-free brain activity, linked with learning, the integration of different time scales, and the formation of mental models, is correlated with a metastable cognitive basis. The spectral slope, a key aspect of scale-free dynamics, was proposed as a potential indicator to distinguish between different sleep stages. Studies suggest that brain networks maintain a consistent scale-free structure across wakefulness, anesthesia, and recovery. Although differences in anesthetic sensitivity between the sexes are recognized, these variations are not evident in clinical electroencephalographic recordings of the cortex. Recently, changes in the slope of the power law exponent of neural activity were found to correlate with changes in Rényi entropy, an extended concept of Shannon's information entropy. These findings establish quantifiers as a promising tool for the study of scale-free dynamics in the brain. Our study presents a novel visual representation called the Rényi entropy-complexity causality space, which encapsulates complexity, permutation entropy, and the Rényi parameter q. The main goal of this study is to define this space for classical dynamical systems within theoretical bounds. In addition, the study aims to investigate how well different time series mimicking scale-free activity can be discriminated. Finally, this tool is used to detect dynamic features in intracranial electroencephalography (iEEG) signals. To achieve these goals, the study implementse the Bandt and Pompe method for ordinal patterns. In this process, each signal is associated with a probability distribution, and the causal measures of Rényi entropy and complexity are computed based on the parameter q. This method is a valuable tool for analyzing simulated time series. It effectively distinguishes elements of correlated noise and provides a straightforward means of examining differences in behaviors, characteristics, and classifications. For the iEEG experimental data, the REM state showed a greater number of significant sex-based differences, while the supramarginal gyrus region showed the most variation across different modes and analyzes. Exploring scale-free brain activity with this framework could provide valuable insights into cognition and neurological disorders. The results may have implications for understanding differences in brain function between the sexes and their possible relevance to neurological disorders.

The construction and theoretical investigation of compound-protein target-pathway network for Radix Pueraria

The complex network approach is an effective method to analyze the global properties of complex biological systems, which can be used to explore the interactions between multiple compounds and protein targets of drug. Radix Pueraria has a long history of efficacy in different diseases, containing a variety of compounds that can produce multiple therapeutic effects via multiple targets. To explore the interrelationship between compounds of Radix Pueraria, protein targets, and pathways, the present investigation used the complex network approach to construct the compound-protein target-pathway network of Radix Pueraria. The analysis of the statistical and topological characteristics shows that the network has small-world and scale-free characteristics. The diameter of the network is 7, indicating that the information exchange between two nodes could quickly spread to the whole network. With the rank of degree value and integrated centrality as identification indicators, by taking the threshold of integrated centrality as 0.70, a total of nine key hub nodes containing three active compounds, two protein targets, and four biological pathways were identified: genistein, daidzein, puerarin, MAPK3, MAPK1, hsa01100, hsa05200, hsa05417, and hsa05207. The network analysis suggests these key hub nodes should play an important role in the pharmacological action of Radix Pueraria. The results of this study provide useful information and knowledge for further exploring the pharmacological mechanism of Radix Pueraria in the course of disease treatment.

Impacts of multiple environmental factors on soil bacterial community assembly in heavy metal polluted paddy fields

Soil microorganisms play pivotal roles in driving essential biogeochemical processes in terrestrial ecosystems, and they are sensitive to heavy metal pollution. However, our understanding of multiple environmental factors interaction in heavy metal polluted paddy fields to shape microbial community assembly remain limited. In the current study, we used 16S rRNA amplicon sequencing to characterize the microbial community composition in paddy soils collected from a typical industry town in Taihu region, eastern China. The results revealed that Cd and Pb were the major pollutant, and Proteobacteria, Acidobacteria and Chloroflexi were the dominate indigenous bacterial phyla. Linear regression and random forest analysis demonstrated that soil pH was the most important predictor of bacterial diversity. Mantel analysis showed that bacterial community structure was mainly driven by pH, CEC, silt, sand, AK, total Cd and DTPA-Cd. The constructed bacterial co-occurrence network, utilizing a random matrix theory-based approach, exhibited non-random with scale-free and modularity features. The major modules within the networks also showed significant correlations with soil pH. Overall, our study indicated that soil physiochemical properties made predominant contribution to bacterial community diversity, structure and their association in Cd/Pb polluted paddy fields. These findings expand our knowledge of the key environmental drivers and co-occurrence patterns of bacterial community in polluted paddy fields.

Identification of risk key factors and prevention strategies for collision accidents between merchant and fishing vessels in China waters based on complex network

In recent years, Collision Accidents between Merchant and Fishing Vessels (CAMF) in China waters have been increasing, resulting in severe casualties and property losses. To effectively prevent such frequent collision accidents and ensure the safety of waterways and navigation routes, this study aims to identify the key risk factors associated with CAMF. Firstly, the Human Factors Analysis and Classification System (HFACS) model is employed as a comprehensive framework to extract the risk factors contributing to CAMF. By introducing edge length as the weight of network edges, a directed weighted network of CAMF is constructed. Secondly, the overall topological characteristics of the network are analyzed, and the network's robustness is studied based on the degree centrality, weighted closeness centrality, and weighted betweenness centrality of the risk factors in the network, and the key factors contributing to the accidents are identified. The research findings indicate that the network exhibits evident scale-free characteristics and small-world network properties. Deliberate attacks on approximately top 15% of the risk factors lead to a 55% degradation in the global efficiency of the accident network. Ultimately, accident prevention and control strategies and recommendations are proposed from the perspectives of both merchant and fishing vessels.

Structural properties of extended pseudo-fractal scale-free network with higher network efficiency

Abstract Complex networks, as an important model for studying complex systems, have been gradually studied in various extension models. Firstly, the network is proved to be sparse by calculating the density of the extending pseudo-fractal network. Secondly, it is proved that the network nodes conform the power-law distribution by enumerate the degree sequence of nodes with power law index $ 2 \lt \gamma \leq 2.585 $ shows that the network has scale-free feature. Thirdly, it is also demonstrated that the average clustering coefficient $ \overline{C_{g}} $ can reach a theoretical lower limit value 0.8182 and the increase of extension coefficient m makes the $ \overline{C_{g}} $ higher than traditional fractal networks. Fourthly, we derive the analytical expression and limit expression of the average path length, and conclude that it has small-world effect. Meanwhile, it is shown that the average path length $ \overline{D_{g}} $ is logarithmically related to Ng growth relationship in Fg. Finally, it is concluded that this extended pseudo-fractal network becomes more effective under the effect of m.

Pattern Analysis of Periodic Sediment Elevation Data: A Decision Support Framework for Deposited Sediment Management in Coastal Environment

This research explores sediment deposition patterns in coastal areas by employing synthetic data representing wandering set and recurrent patterns, utilizing Fourier transformation and power-law analysis to characterize frequency content and scaling behavior. The study employs fundamental models to interpret observed patterns and investigates the relevance of Poincaré's theorem in understanding recurrent patterns. Leveraging Python-based computation systems and open-source satellite data, the findings reveal contrasting behaviors in power-law exponents between scenarios, with wandering set patterns exhibiting scale-free characteristics and recurrent patterns displaying structured frequency distributions. This holistic approach advances scientific knowledge on sediment dynamics, informing evidence-based decision-making for effective sediment management and coastal resilience planning. Through its methodology and insights, the research contributes to understanding sediment deposition processes, elucidating underlying mechanisms driving coastal sedimentation, and providing practical approaches for monitoring and studying sediment dynamics in coastal environments, ultimately enhancing strategies for coastal management and conservation.

Construction and Characteristics Analysis of the Xi’an Public Transport Network Considering Single-Mode and Multi-Mode Transferring

The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a “green city”. On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between ‘bus–bus’, ‘metro–metro’, and ‘metro–bus’ based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi’an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi’an’s metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi’an has the highest degree of direct access, and the core areas of the south, west, and north of Xi’an also have high direct access. However, the direct access in the area east of Xi’an is slightly lower. (4) Xi’an’s bus transport network covers a large area, showing the characteristics of a dual-core “central + southern” network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of “the single core is dominant, and the circle diffusion weakens step by step”.

Intra-community link formation and modularity in ultracold growing hyperbolic networks

Hyperbolic network models offer a straightforward yet powerful method for understanding the small-world, scale-free, highly clustered, and modular characteristics typical of complex systems that are often called as real-world networks. These models involve randomly positioning nodes in a hyperbolic space and connecting them based on a probability that diminishes with distance. In this study, we examine the community structure within networks created by the Popularity Similarity Optimization model, a fundamental hyperbolic model, when the temperature parameter (responsible for tuning the clustering coefficient) is set to the limiting value of zero. We focus on link formation within communities and show their close relation with non-linear preferential attachment processes. Based on this, we derive analytical expressions that significantly improve previous estimates of the expected modularity for partitions formed by equally sized angular sectors in the 2d hyperbolic space. Our formulas can now predict average modularity, confirmed by numerical simulations, with high accuracy over a broader range of model parameters and community sizes relative to the whole network. These results advance our understanding of module formation in hyperbolic networks, highlighting the surprising emergence of communities despite the lack of explicit community formation steps in the model definition.

Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling

Recent results have evidenced that spontaneous brain activity signals are organized in bursts with scale free features and long-range spatio-temporal correlations. These observations have stimulated a theoretical interpretation of results inspired in critical phenomena. In particular, relying on maximum entropy arguments, certain aspects of time-averaged experimental neuronal data have been recently described using Ising-like models, allowing the study of neuronal networks under an analogous thermodynamical framework. This method has been so far applied to a variety of experimental datasets, but never to a biologically inspired neuronal network with short and long-term plasticity. Here, we apply for the first time the Maximum Entropy method to an Integrate-and-fire (IF) model that can be tuned at criticality, offering a controlled setting for a systematic study of criticality and finite-size effects in spontaneous neuronal activity, as opposed to experiments. We consider generalized Ising Hamiltonians whose local magnetic fields and interaction parameters are assigned according to the average activity of single neurons and correlation functions between neurons of the IF networks in the critical state. We show that these Hamiltonians exhibit a spin glass phase for low temperatures, having mostly negative intrinsic fields and a bimodal distribution of interaction constants that tends to become unimodal for larger networks. Results evidence that the magnetization and the response functions exhibit the expected singular behavior near the critical point. Furthermore, we also found that networks with higher percentage of inhibitory neurons lead to Ising-like systems with reduced thermal fluctuations. Finally, considering only neuronal pairs associated with the largest correlation functions allows the study of larger system sizes.

Interface engineering for facile switching of bulk-strong polarization in Si-compatible vertical superlattices

Ferroelectric thin films incorporating different compositional layers have emerged as a promising approach for enhancing properties and performance of electronic devices. In recent years, superlattices utilizing various interactions between their constituent layers have been used to reveal unusual properties, such as improper ferroelectricity, charged domain walls, and negative capacitance in conventional ferroelectrics. Herein, we report a symmetry scheme based on the interface engineering in which the inherent cell-doubling symmetry allowed atomic distortions (phonons) in any vertically aligned superlattice activate novel interface couplings among atomic distortions of different symmetries and fundamentally improve the ferroelectric properties. In a materialized case, the ionic size difference between Hf4+ and Ce4+ in the HfO2/CeO2 (HCO) ferroelectric/paraelectric superlattice leads to these couplings. These couplings mitigate the phase boundary between polar and non-polar phases, and facilitate polarization switching with a remarkably low coercive field (Ec\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{c}$$\\end{document}) while preserving the original magnitude of the bulk HfO2 polarization and its scale-free ferroelectric characteristics. We show that the cell-doubled distortions present in any vertical superlattice have unique implications for designing low-voltage ferroelectric switching while retaining bulk-strong charge storing capacities in Si-compatible memory candidates.

Lorentz equivariant model for knowledge-enhanced hyperbolic collaborative filtering

Introducing prior auxiliary information from the knowledge graph (KG) to assist the user–item graph can improve the comprehensive performance of the recommender system. Many recent studies have shown that the ensemble properties of hyperbolic spaces fit the scale-free and hierarchical characteristics exhibited in the above two types of graphs well. Therefore, hyperbolic-based recommender systems have achieved a series of outstanding performances. However, in existing hyperbolic methods, equivariance is not considered. Thus, they cannot generalize symmetric features under given transformations, which seriously limits the capability of the model. Moreover, they cannot balance preserving the heterogeneity and mining the high-order entity information to users across two graphs. To fill these gaps, we propose a rigorous Lorentz group equivariant knowledge-enhanced collaborative filtering (LECF) model. Innovatively, we jointly update the attribute embeddings (containing the high-order entity signals from the KG) and hyperbolic embeddings (the distance between hyperbolic embeddings reveals the recommendation tendency) via the LECF layer with Lorentz Equivariant Transformation. Moreover, we propose Hyperbolic Sparse Attention Mechanism to sample the most informative neighboring nodes. Lorentz equivariance is strictly maintained throughout the entire model, and enforcing equivariance is proven necessary experimentally. Extensive experiments on three real-world datasets demonstrate that the proposed LECF remarkably outperforms state-of-the-art methods.

The structural and spatial evolution of the China Railway Express network

To illustrate the structural and spatial evolution of the China Railway Express (CR-Express) network, this paper adopts the Space-P method to construct three complex networks of the CR-Express routes respectively by the block train data in 2013, 2016 and 2019, and the indices of overall network structure (average degree, average path length, and average clustering coefficient) and indices of node importance (degree centrality and betweenness centrality) in the networks are comparatively analyzed. It is shown that the CR-Express network has become more complicated, but in 2016 the overall structure was basically stable, and consistently presents clear small-world and scale-free characteristics. The nodes in the network can be partitioned into four groups based on their influence on network connectivity and the number of block trains departing from them, namely core nodes, freight demand nodes, key nodes and edge nodes. Among them, core nodes and freight demand nodes are located in the central and western regions during the whole study period, and the newly added nodes in the eastern region from 2016 to 2019 are edge nodes. Based on the above analysis, we capture the evolving nature of the CR-Express network across distinct development stages under the influence of policy, economy, geographical location and other factors, and put forward operational suggestions to optimize the CR-Express service, strengthen the maintenance of key nodes, and ensure the efficient operation of China-Europe trains.

Impacts of the Middle Route of China's South-to-North Water Diversion Project on the water network structure in the receiving basin.

The Middle Route of South-to-North Water Diversion Project (MRSNWD) is the main skeleton of China's National Water Network, its construction has changed the structure of the original water network, and analyzing the topological change of the water network in context with MRSNWD is significant for water network planning and management. In this study, the overall network characteristics of the water network in 2010 and 2020 were analyzed based on the small-world and scale-free characteristics of complex network theory. The topological changes of the water network from a node perspective were examined using three network centrality indexes: degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC), while assessing the important nodes of the water network and recognizing functional areas of cold-hot spots. The results show that the water network's centrality in the study area improved after the project construction, with the average degree of the water network increasing from 2.39 to 2.42 and the average path length decreasing from 111.81 to 97.08. The propagation efficiency and network stability also increased, with a rise in important node proportion from 9.8 to 14.4%. The nodes in the DC hotspot zone along the project route have increased by 1.5%, implying an increase in the connectivity of the water network, while MRSNWD optimizes its north-south hub propagation path. "Small-worldness" indicates that most nodes of a network can be accessed and connected over shorter paths. The water network has a significant "small-worldness" and has been enhanced by the MRSNWD's construction. Approximating the water network as a scale-free network can impact its security by identifying critical nodes. The results of this research can provide the necessary technical support and reference significance for China's National Water Network.

Excitation-Inhibition Balance, Neural Criticality, and Activities in Neuronal Circuits.

Neural activities in local circuits exhibit complex and multilevel dynamic features. Individual neurons spike irregularly, which is believed to originate from receiving balanced amounts of excitatory and inhibitory inputs, known as the excitation-inhibition balance. The spatial-temporal cascades of clustered neuronal spikes occur in variable sizes and durations, manifested as neural avalanches with scale-free features. These may be explained by the neural criticality hypothesis, which posits that neural systems operate around the transition between distinct dynamic states. Here, we summarize the experimental evidence for and the underlying theory of excitation-inhibition balance and neural criticality. Furthermore, we review recent studies of excitatory-inhibitory networks with synaptic kinetics as a simple solution to reconcile these two apparently distinct theories in a single circuit model. This provides a more unified understanding of multilevel neural activities in local circuits, from spontaneous to stimulus-response dynamics.

A network model of social contacts with small-world and scale-free features, tunable connectivity, and geographic restrictions.

Small-world networks and scale-free networks are well-known theoretical models within the realm of complex graphs. These models exhibit "low" average shortest-path length; however, key distinctions are observed in their degree distributions and average clustering coefficients: in small-world networks, the degree distribution is bell-shaped and the clustering is "high"; in scale-free networks, the degree distribution follows a power law and the clustering is "low". Here, a model for generating scale-free graphs with "high" clustering is numerically explored, since these features are concurrently identified in networks representing social interactions. In this model, the values of average degree and exponent of the power-law degree distribution are both adjustable, and spatial limitations in the creation of links are taken into account. Several topological metrics are calculated and compared for computer-generated graphs. Unexpectedly, the numerical experiments show that, by varying the model parameters, a transition from a power-law to a bell-shaped degree distribution can occur. Also, in these graphs, the degree distribution is most accurately characterized by a pure power-law for values of the exponent typically found in real-world networks.

Protein-protein interaction networks reveal therapeutic targets in Khlu, Hoary basil and Indian mulberry for inflammation, diabetes and diarrhea

Importance of the work: Khlu, Hoary basil and Indian mulberry leaves have important medicinal properties for treating inflammation, diabetes and diarrhea. Objectives: To elucidate the roles and molecular mechanisms of Khlu, Hoary basil and Indian mulberry leaf contents based on protein-protein interaction network analysis. Materials & Methods: Protein-protein interaction networks were constructed for Khlu, Hoary basil and Indian mulberry leaves using databases (ChEMBL, STITCH, STRING and DisGeNET). The developed networks, exhibiting scale-free, small-world and modular characteristics, were analyzed to identify and interpret disease-related modules for inflammation, diabetes and diarrhea. Results: Leaves from Khlu, Hoary basil and Indian mulberry influenced biological processes linked to inflammatory mediators, insulin resistance and colon fluid secretion, suggesting potential mechanisms to prevent inflammation, diabetes and diarrhea. Active ingredients from these leaves formed interaction networks with characteristics conducive to module analysis, identifying target protein modules for these diseases. Common biological processes —lipid metabolism, metal ion transport and adenosine metabolism —impacted insulin resistance, glucose regulation and inflammatory mediator production. Main finding: Plausible mechanisms supporting their effectiveness were development for Khlu, Hoary basil and Indian mulberry leaves that are traditionally used by local people for treating inflammation, diabetes and diarrhea. Protein-protein interaction network analysis revealed their influence on inflammatory mediators, insulin resistance and glucose regulation. The identified protein groups could be valuable targets for future research on their medicinal properties.