Node Connectivity Research Articles

Flexible and Advanced DBS: A distributed control strategy for a DC[sbnd]Nanogrid

Nowadays, it is well known that the energy system is going towards a clear and necessary energy transition. New enabling technologies are needed to accelerate this transition, like the dc nanogrids. The dc nanogrids are designed for a bottom-up approach and play an important role due to their scalability and modularity features. To take advantage of these features, in this paper, the authors propose a flexible and advanced DBS distributed control strategy so to be independent from the dc nanogrid configuration and operating both in stand-alone and in the grid-connected mode. At this scope the dc nanogrid is modelled as the connection of multiple generic nodes and a hierarchical control architecture is applied as a single control structure. It facilitates the seamless transition between the operating modes, independently from the dc nanogrid configuration. The effectiveness of the proposed flexible and advanced DBS is validated by some experiments performed on a testbed integrating a dc nanogrid laboratory prototype.

Exploring scale-free networks: Survey on autonomous system dynamics and connectivity analysis

The global Internet is continuously developing; however, it is becoming more complex with time owing to the introduction of new nodes and links among autonomous systems (ASes). The network links among ASes appear and disappear, especially when the shortest path is required or a low-cost upstream Internet service provider (ISP) is a prerequisite. Understanding the Internet dynamics is critical for improving Internet characteristic analysis. In this work, we reviewed the existing studies from the last two decades on AS network topology as well as relations and routing among ASes. Similarly, we measured the link value differences within Russia and Central Asian ASes using the Barabasi–Albert (BA) model. Our analysis results show that in the last two years, the number of newly added links among 5377 ASes is 65,782. Further, the results show that the ASes with a high network degree in terms of high connectivity or preferential attachment approach are those used by the largest telecom companies in Russia and Central Asian regions. This survey paper aimed to determine whether the Internet AS graphs in these regions adhere to scale-free network properties, and we further examined the growth patterns of node connectivity within these regions.

Navigating the Web: A Study on Network Topology and Information Flow

The internet, as a complex network of interconnected nodes, presents a dynamic landscape for the dissemination and flow of information. This study delves into the intricate interplay between network topology and information propagation, aiming to elucidate the mechanisms underlying navigation through the web. By analyzing various network models and employing techniques from graph theory, we investigate how the structural characteristics of the web influence the spread of information. Furthermore, we explore the role of key factors such as node centrality, clustering, and connectivity patterns in shaping information diffusion dynamics. Through empirical analysis and computational simulations, our findings shed light on the mechanisms governing the flow of information across the web, offering insights into strategies for effective navigation and content dissemination in the digital age.

ISSUES OF DESIGNING INDUSTRIAL BUILDINGS CONSIDERING THE RISK OF LOCAL DESTRUCTION OF THEIR BEARING STRUCTURES

Problem statement. The present-day realities show that the design of buildings and structures with regard to possible local collapses of their bearing structures is becoming more and more realistic and thus an increasingly urgent challenge. The need to solve this issue is an important component in ensuring the safety of buildings and structures under operational and emergency loads. Local destruction is interpreted as a global result that can lead to the destruction of the entire frame or a significant part of it and cause loss of life. The peculiarity of calculations for progressive collapse of steel-framed buildings is that, unlike monolithic reinforced concrete, in a steel frame, the nodes of connection of elements are usually hinged, and this can lead to transformation of the structural scheme into a geometrically variable structural scheme. In this case, structural failure may occur not due to loss of material strength, but due to loss of load-bearing capacity of the structural connection joints. The purpose of the article is to analyse the main specific peculiarities occurring in the design (reinforcement) of industrial buildings with steel frames, considering the possibility of their local destruction. Conclusion. The article considers some problems that need to be solved during the designing or reconstructioning industrial buildings with a steel frame, the operation of which may be associated with some local destruction of the bearing structures. Prospective constructive ways of effectively solving these problems are presented.

DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network

Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not the node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology structures in a payment network may reveal sellers’ business roles (e.g., supplier or retailer). To model both connectivity and local topology structure for better node classification performance, we present DP-GCN, a dual-path graph convolution network. DP-GCN consists of three main modules: (i) a C-GCN module to capture the connectivity relationships between nodes, (ii) a T-GCN module to capture the topology structure similarity among nodes, and (iii) a multi-head self-attention module to align both properties. We evaluate DP-GCN on seven benchmark datasets against diverse baselines to demonstrate its effectiveness. We also provide a case study of running DP-GCN on three large-scale payment networks from PayPal, a leading payment service provider, for risky seller detection. Experimental results show DP-GCN’s effectiveness and practicability in large-scale settings. PayPal’s internal testing also shows DP-GCN’s effectiveness in defending against real risks from transaction networks.

Identifying influential nodes based on the disassortativity and community structure of complex network

The complex networks exhibit significant heterogeneity in node connections, resulting in a few nodes playing critical roles in various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential nodes that play crucial roles in networks is very important. Many methods have been proposed in different fields to solve this issue. This paper focuses on the different types of disassortativity existing in networks and innovatively introduces the concept of disassortativity of the node, namely, the inconsistency between the degree of a node and the degrees of its neighboring nodes, and proposes a measure of disassortativity of the node (DoN) by a step function. Furthermore, the paper analyzes and indicates that in many real-world network applications, such as online social networks, the influence of nodes within the network is often associated with disassortativity of the node and the community boundary structure of the network. Thus, the influential metric of node based on disassortativity and community structure (mDC) is proposed. Extensive experiments are conducted in synthetic and real networks, and the performance of the DoN and mDC is validated through network robustness experiments and immune experiment of disease infection. Experimental and analytical results demonstrate that compared to other state-of-the-art centrality measures, the proposed methods (DoN and mDC) exhibits superior identification performance and efficiency, particularly in non-disassortative networks and networks with clear community structures. Furthermore, we find that the DoN and mDC exhibit high stability to network noise and inaccuracies of the network data.

Application of Graph Theory for Blockchain Technologies

Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.

Graph convolution network-based surrogate model for natural convection in annuli

This work develops a model for natural convection in annuli with internal heat sources based on Graph Convolution Network (GCN), achieving rapid prediction by directly establishing the mapping relationship between the geometric features and the temperature field. The GCN learns node features and connections between nodes in data structures. Compared with traditional machine learning networks, GCN exhibits greater advantages in handling complex geometric shapes and unstructured nodes. In this study, numerous data samples were constructed with various geometric features, e.g., varying sizes, positions, and quantities of internal heat sources. The results indicate that a fully trained GCN was capable to adaptively predict temperature field distributions with arbitrary heat source sizes, positions, and quantities. The mean prediction errors of GCN for the temperature field are around 0.17 %, 0.32 %, and 0.85 %, for the single, double, and triple heat sources cases, respectively. Furthermore, the GCN prediction performance was also compared with surrogate models by the convolutional, and fully connected neural networks (CNN and FNN), which shows an improvement of 88.1 % and 84.8 %, respectively. Therefore, compared with numerical results and other surrogate models, the proposed model presents superior geometric adaptability, high prediction accuracy and a remarkable enhancement in computational efficiency (three orders of magnitude).

Evolution of cosmic filaments in the MTNG simulation

We present a study of the evolution of cosmic filaments across redshift with an emphasis on some important properties: filament lengths, growth rates, and radial profiles of galaxy densities. Following an observation-driven approach, we built cosmic filament catalogues at z = 0, 1, 2, 3, and 4 from the galaxy distributions of the large hydro-dynamical run of the MilleniumTNG project. We employed the extensively used DisPerSE cosmic web finder code, for which we provide a user-friendly guide, including the details of a physics-driven calibration procedure, with the hope of helping future users. We performed the first statistical measurements of the evolution of connectivity in a large-scale simulation, finding that the connectivity of cosmic nodes (defined as the number of filaments attached) globally decreases from early to late times. The study of cosmic filaments in proper coordinates reveals that filaments grow in length and radial extent, as expected from large-scale structures in an expanding Universe. But the most interesting results arise once the Hubble flow is factored out. We find remarkably stable comoving filament length functions and over-density profiles, showing only little evolution of the total population of filaments in the past ∼12.25 Gyr. However, by tracking the spatial evolution of individual structures, we demonstrate that filaments of different lengths actually follow different evolutionary paths. While short filaments preferentially contract, long filaments expand along their longitudinal direction with growth rates that are the highest in the early, matter-dominated Universe. Filament diversity at a fixed redshift is also shown by the different (∼5σ) density values between the shortest and longest filaments. Our results hint that cosmic filaments can be used as additional probes for dark energy, but further theoretical work is still needed.

Connection based novel AL topological descriptors and structural property of the zinc oxide metal organic frameworks

Metal organic frameworks (MOFs) play a vital role in a variety of processes such as heterogeneous catalysis, heterogeneous gas purification and division, biocompatibility, toxicity, and biomedical applications. The Zinc-based MOFs, particularly, have gained prominence in the biomedical industry for applications such as drug delivery, biosensing, and cancer imaging, which has attracted the interest of researchers from around the world. Given the significant influence of molecular structure on the properties of MOFs, it is imperative to model and characterize this structure comprehensively to deepen our understanding and explore avenues for property enhancement. Topological indices are numerical parameters derived from the graph representation of molecules that quantify specific structural features. These indices provide insights into the connectivity and arrangement of atoms or nodes within the structure, offering information about its chemical, physical, or biological properties. In this study, the Zinc Oxide network structures are topologically modeled via connection numbers, and some novel topological descriptors viz. AL 1, AL 2, AL3, AL4, AL5, AL6, AL7andAL8 are computed by deriving mathematical closed form expressions for them. Based on the numerical results, a comparative analysis of the computed topological descriptors is also performed. The prediction potential and chemical applicability of these indices are investigated using the benchmark dataset of Octane Isomers and corresponding experimental values, correlating experimental and computed values through regression models.

Анализ напряженно-деформированного состояния узловых соединений высотных зданий при учете климатических воздействий

Purpose: to analyze the design solutions of the junctions of floors with external walls in buildings erected using monolithic technology. To identify defects and damages that occur on facades and overlaps with perforations for thermal pads during construction and operation. To investigate by numerical methods the stress-strain state of the overlap disk at the junction with the outer wall, taking into account temperature influences. Materials and methods: the standard version of the nodal connection of a monolithic building of various storeys is considered. The fragment of the junction joint of the overlap disk with the wall was modeled in the Ansys and SCAD software complexes. The model consisted of an anisotropic material — concrete and reinforcement rods included in it. The behavior of the stress-strain state under various temperature influences was studied. Results: numerically, taking into account geometric parameters and temperature loads, the patterns affecting the nature of changes in the values of normal stresses in the characteristic sections of the perforated plate are revealed and the existing types of design solutions are evaluated. The locations of stress concentration zones and their impact on performance are determined. Practical significance: it is shown that the most dangerous cross-section in perforated discs of overlaps takes place at the key joints. It is determined that vulnerability in such compounds is caused by the influence of alternating cyclic temperatures. Patterns leading to an increase in normal stresses at stress concentration sites have been established. Fundamentally new design solutions have been proposed, protected by utility model patents, which allow to reduce stress values and thereby ensure higher performance in monolithic buildings, including high-rise ones.

GUARANTEED ORIGIN OF ELECTRICITY IN THE LOCAL POWER SYSTEM WITH RENEWABLE ENERGY SOURCES

The article is focused on the development of a method for determining the part of electricity generated by renewable energy sources (RES) in the electricity flows in the branches of the electric grid. The method is based on a mathematical model of the electric grid, in which the coefficients of current distribution in the branches of the circuit from RES nodes and nodal voltages are used to determine the electricity flows in the branches of the electric grid. As a result, a matrix of power distribution coefficients of RES generation units to load nodes of the power grid is formed. This way, the component of the load power that depends on the power in the RES nodes is determined. The voltages used to form the RES power distribution matrix are determined based on the results of calculating the steady-state modes of the power grid or on the measurement data of the ACEMS. Since the forming of local electric power systems (LES) based on RES is actual, the article considers LES as a balancing group within the electric power system (EPS), which can operate as an autonomous system according to certain conditions. It is shown that for this purpose, a RES reservation system should be created in the power system due to their dependence on natural conditions and, accordingly, the instability of their electricity generation. When a transmission system operates as part of an EPS, a general system reserve is usually used (mainly maneuverable capacities of thermal and hydroelectric power plants). In order for the LES can operate as an autonomous one, it is proposed to use other means of power reserve available today: electrochemical energy storage, hydrogen and biogas technologies. An effective way is to coordinate the schedules of RES generation in the LES with the load schedules of active consumers. The example shows that in the case of a power deficit in the LES, RES located in the power grid can be used. The values of the necessary power at the LES connection node, power flows in the branches connecting the LES and RES grid, and power losses in them are determined by the offered method.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Whole-brain correlates of post-traumatic stress disorder in military-related mild traumatic brain injury: A voxel-wise meta-analysis

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used in studying mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD) among military service personnel (MSP). The risk for PTSD is greater after mTBI than after non-mTBI trauma, and PTSD is associated with worse biological, psychological, and social (biopsychosocial) outcomes after mTBI. Studying the neural correlates of mTBI-related PTSD in this population may provide insights into the pathophysiology of both conditions and help identify MSPs most at risk of developing persistent symptoms. This voxel-wise meta-analysis aimed to examine the current literature on rs-fMRI in mTBI-related PTSD, collect neuroimaging data, and perform a voxel-wise meta-analysis to identify significant nodes of functional connectivity across studies. We searched the literature using PubMed, EMBASE, and ScienceDirect. The database query yielded 1455 articles, narrowed with specified eligibility criteria to a final cohort of 33 studies, with 1521 experiment group participants and 1369 controls. Article quality was assessed using the QUADAS-2 instrument. Our meta-analysis revealed a robust correlation between PTSD-mTBI and increased whole-brain resting-state functional connectivity (rsFC) in the amygdala. Our meta-analysis also showed strong correlations between PTSD-mTBI and decreased whole-brain rsFC across regions implicated in speech and language processing. Our meta-regression highlighted robust correlations between less time after a recent mTBI and increased whole-brain rsFC in posterior regions of the default mode network. Our meta-regression also showed strong correlations between increased PTSD symptom severity, increased connectivity in the amygdala, and decreased connectivity in Broca's area.

Examining link prediction and node connectivity objectives in social networks: Comprehensive review

A social network plays a pivotal role in modern society, facilitating communication, information dissemination and social interactions. As systems such as this one continues to grow in complexity and scope, yet the challenges become identifying the absence of a node as well as ensuring an uninterrupted connection. These comprehensive reviews analyze the objectives of link prediction and node connectivity in the context of social networks. The objective of link prediction and node connectivity in social networks is to pave the way for future analysis in network design and security. This review analyze the node connectivity and link prediction, which is represented as graph G= (s, e) where s denotes the number of vertices and e denotes the number of edges. It highlights the effectiveness of linkages along with node connectivity and forecasting future interactions in enabling the development of targeted marketing strategies and personalized recommendation systems. The significance of node connectivity analysis is to evaluate the resilience and robustness of social networks. It explores the identification of critical nodes and the impact of their removal on network fragmentation, thus emphasizing the importance of developing effective strategies to enhance network resilience and security. Furthermore, the review emphasizes the interplay between link prediction and node connectivity in advancements of link prediction techniques that can booster the accuracy of node connectivity in social networks. It underscores the implications to understand the network dynamics as well as predicting the development of security and efficient social network infrastructures.

Topological Structure and Control Strategy of E-UPFC

This article proposes a unified power flow controller with energy (E-UPFC) installed at the renewable energy grid connection node of the transmission network. Compared with other unified power flow controllers (UPFCs), the proposed E-UPFC can not only regulate the power flow on its connected transmission lines, but also suppress the power fluctuations of grid-connected nodes injected with large-scale renewable energy. At the same time, with the installation position of E-UPFC transferred from the transmission line to the node, the original stiff node can be transformed into a flexible node that can regulate the injected power flexibly. First, the PV grid-connected system with E-UPFC is introduced, and its principle of power flow regulation is detailed. In addition, the topology, mathematical modeling, and control strategies of E-UPFC are discussed. Finally, the E-UPFC is applied to the IEEE 3-generator 9-bus system with large-scale renewable energy integration in Matlab/Simulink in order to verify the correctness and feasibility of E-UPFC and its control strategies.

High gain antipodal meander line antenna for point-to-point WLAN/WiMAX applications

This paper introduces a planar antipodal meander line antenna fabricated using RO3003 substrate. The proposed antenna is designed to radiate in the end-fire direction, achieving a maximum measured gain of 10.43 dBi within its working bandwidth, which ranges from 2.24 GHz to 2.7 GHz, covering long-range WLAN/WiMAX applications. A systematic procedure is adopted in the design process to prove its tunability to cover other application requirements in terms of gain and bandwidth. The proposed design steps show that the bandwidth and the gain can independently be controlled by adjusting specific design parameters such as the number of radiators and the scaling factor. The antenna is fine-tuned using sensitivity analysis and parametric study to guarantee optimum operation at the desired bandwidth. Experimental measurements of the fabricated design demonstrate a high degree of correlation with simulations conducted using CST and HFSS. In comparison to other end-fire antennas presented in the literature, the proposed design manifests its capability to provide high gain for WLAN/WiMAX nodes connectivity. The measurements show that the proposed traveling wave antenna exhibits a high radiation efficiency with maximum value of 99.8% at 2.35 GHz. The measured side lobe level is found to be below -18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-18$$\\end{document} dB and -20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-20$$\\end{document} dB at 2.45 GHz along the E\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$E$$\\end{document}- and H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H$$\\end{document}-planes, respectively. Apart from its excellent radiation performance, the antenna is characterized by its low profile and fabrication simplicity.

ENHANCING URBAN PATHFINDING FOR PEDESTRIANS THROUGH FUSION OF MLS AND HMLS DATA

Abstract. Pedestrian pathfinding is crucial for enhancing pedestrian mobility in urban environments. In this research a method to generate navigation graphs based on Mobile Laser Scanning (MLS) and Handheld Laser Scanning (HMLS) data fusion is developed. The input data comprises a 2-kilometer urban street network that integrates both MLS and HMLS data, effectively mitigating sidewalk occlusions caused mainly by parked vehicles. The proposed method encompasses the following steps: (1) Deep Learning semantic segmentation of urban ground elements and navigation-related obstacles, (2) Static vs. Dynamic object differentiation to replicate conditions of unobstructed passage. (3) Sidewalk enrichment with inclination, preservation status, and width. (4) Physical accessibility estimation between sidewalks and crosswalks incorporating curb information. And (5) navigation graph computation based on the enhanced sidewalk data, crosswalks, with accurate node location and connections. Routes were calculated within a Geographical Information System, and results ensure that pedestrians can navigate in urban environments with precision and efficiency.

Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

As one of the core modules for air traffic flow management, Air Traffic Flow Prediction (ATFP) in the Multi-Airport System (MAS) is a prerequisite for demand and capacity balance in the complex meteorological environment. Due to the challenge of implicit interaction mechanism among traffic flow, airspace capacity and weather impact, the Weather-aware ATFP (Wa-ATFP) is still a nontrivial issue. In this paper, a novel Multi-faceted Spatio-Temporal Graph Convolutional Network (MSTGCN) is proposed to address the Wa-ATFP within the complex operations of MAS. Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather, which can effectively address the complex impact of severe weather, e.g., thunderstorms. Thirdly, to capture the latent connections of nodes, an adaptive graph connection constructor is designed. The experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area, China, validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance. The case study of convective weather scenarios further proves the adaptability of the proposed approach.

The Interplay Between Epilepsy and Parkinson's Disease: Gene Expression Profiling and Functional Analysis.

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein-protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor-micro-RNA regulatory and co-regulatory networks, the gene-microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets.