Girvan-Newman Algorithm Research Articles

A Social Network Analysis on Abu Sayyaf Kidnappings

The Abu Sayyaf Group (ASG) is a Philippine-based extremist organization globally known for engaging in terrorism, kidnappings, and various criminal activities. This study sought to comprehensively analyze Abu Sayyaf ’s kidnapping incidents using social network analysis to better understand their operational structures, patterns, and dynamics. The researchers applied centrality measurements to assess the significance of nodes in the network, along with the GirvanNewman algorithm for community detection to identify groups sharing similar characteristics and extract groups for various purposes. The findings revealed one kidnapping node to have the highest centrality score and holding the most significant influence and numerous incoming connections within the network. Another kidnapper node in the analysis of out-degree centrality displayed a proactive role, initiating multiple connections and shaping the network’s dynamic. The community detection uncovered seven distinct communities within the network, each demonstrating unique patterns and characteristics. The findings imply that kidnappings were conducted through groups rather than individuals. The majority of networks involving ASG members participating in multiple kidnapping events hold significant implications for national security strategies.

A complex network-based approach for resilient and flexible design resource allocation in industry 5.0

The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence of the Industrial Metaverse, which blurs the boundaries between real and virtual design and manufacturing, it is gaining increasing attention. To embrace the advent of Industry 5.0 and the Industrial Metaverse, swift collaborative cloud services for design and manufacturing resources are essential. In this context, this article introduces a novel approach combining complex networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), aimed at rapidly optimizing the dynamic allocation of distributed design resources (DRs). Initially, a multipartite graph is created from raw data and mapped to multiple bipartite graphs to identify key nodes in the network through intersection. Subsequently, these key nodes are used as reference points in the NSGA-III algorithm to achieve high-quality cloud service combinations, meeting the needs of design tasks with multiple subtasks, and related multi-objective optimization, including time, cost, reliability, maintainability, and reputation associated with the design. Finally, the Pareto service combinations obtained are used to construct a new complex network and employ the Girvan-Newman algorithm based on edge betweenness to identify community structures. In case of anomalies in the best service combination, alternative options can be swiftly searched from the identified communities, thereby enhancing the resilience of the cloud service process. Experimental results demonstrate the method's advantages in recovery and robustness, contributing significantly to the optimization of rapid cloud service allocation for DRs in the context of Industry 5.0.

Collective cognition based analysis of community structure discovery algorithms

Social network topology can shape collective cognition and group behavior. Different social network topologies can facilitate various forms of collective cognition, leading to diverse collective cognition and group function. We analyzed the characteristics of contract networks and compared the performance of community structure discovery algorithms in social networks, using modularity as the assessment index. By examining the speed and effectiveness of these algorithms, we found that the Louvain algorithm and Girvan–Newman algorithm are suitable for discovering the network structure of sparse social networks. Experimental results have shown that the Louvain algorithm outperforms the Girvan–Newman algorithm on sparse networks across multiple scales. Finally, we learned a close relationship between collective cognition and community structure in contract networks, particularly influenced by the central nodes within these communities.

A Novel Distributed Process Monitoring Framework of VAE-Enhanced with Deep Neural Network

Intelligent manufacturing process needs to adopt distributed monitoring scenario due to its massive, high-dimensional and complex data. Distributed process monitoring has been introduced into global monitoring and local monitoring to analyze the characteristic relationship between process data. However, the existing framework methods ignore or suppress the fault information and thus cannot effectively identify the local faults and the time sequence characteristics between units in the batch production system. This paper proposes a novel distributed process monitoring framework based on Girvan-Newman algorithm modular subunit partitioning and probabilistic learning model with deep neural networks. First, Girvan-Newman algorithm is used to divide the complex manufacturing system modularized to reduce the latitude of data processing. Second, variational autoencoder (VAE) is adopted to ensure the stability of local analysis, and long short-term memory is adopted to improve the VAE model to detect global multi-time scale anomalies. Finally, distributed process fault detection is carried out for each subunit in a separate and integrated manner, and the performance of the framework in distributed process monitoring is analyzed through two fault detection indicators T2 and SPE statistics. A case study of the Tennessee Eastman Process is used to demonstrate the performance and applicability of the proposed framework. Results show that the proposed VAE enhancement framework based on the DNN could accurately identify faults in distributed process monitoring and locate the specific sub-units where the fault occurs. Compared with VAE-DNN method and traditional process monitoring methods, the framework proposed in this paper has higher fault detection rate and lower false alarm rate, and the detection rate of some faults can reach 100%.

District metered areas determination for water distribution networks using improved Girvan-Newman algorithm

In general, partitioning networks into district metered areas (DMA) is known as a practical method to manage the pressure and reduce water leakage in Water Distribution Networks (WDNs). Different methods such as engineering judgment, optimization techniques, and graph theory are proposed to determine the DMA. The graph theory is a traditional method for partitioning networks including several algorithms. One of these algorithms is the Girvan-Newman (GN) algorithm which is based on the mathematical parameters without considering the characteristics of networks. In other words, the hydraulic quality and quantity condition of WDNs are not used for DMA determination. Therefore, in this research, a new method is proposed to improve the performance of the GN algorithm for DMA determination considering the quantities of water networks. For this purpose, the average values of nodal pressure and residual chlorine concentration are calculated and used simultaneously for the junction’s weight determination. Then, the edge scores are calculated based on the junction’s weights, and the edges with the maximum scores are removed until to reach the desired number of DMAs. For comparison purposes, here, the Demand-Driven Simulation Method (DDSM) and Head-Driven Simulation Method (HDSM) analyses are used for the analysis of the Poulakis WDN, selected as a case study. A comparison of the results shows that by using the proposed method, the average pressure values are decreased and the average residual chlorine concentration values are increased. In other words, the pressure values are decreased from 16.32% to 26.23% and the average residual chlorine concentration values are increased from 2.5% to 18.37% in comparison with the results of the standard form of the GN algorithm using both DDSM and HDSM analyses. Furthermore, by using the proposed method, in all scenarios, the hybrid reliability values increase in comparison with the GN algorithm. The results indicate the unique performance of the proposed method in comparison with the standard form of the GN algorithm for DMA determination of WDNs.

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.

Optimal Design and Configuration Strategy for the Physical Layer of Energy Router Based on the Complex Network Theory

The energy router (ER) is key to realizing the coordinated management and efficient utilization of multiple forms of energy in Energy Internet. This paper proposes a novel design and configuration method for the physical layer of ER by using complex network theory. Firstly, an abstract model of the physical layer of ER is introduced according to its function, and important modules in the model are illustrated in details. Secondly, based on the electrical characteristics of ports, the community structure of the power networks inside ER is analyzed by the improved Girvan-Newman (GN) algorithm to design the independent bus systems (IBSs) and generate the network topology of the physical layer. Then, the optimization model of equipment configuration of the power supply and distribution systems of ER is developed considering the economy, utilization efficiency, and power supply reliability. Finally, two case studies demonstrate that the proposed strategy can effectively accomplish the module-level structure design and device configuration for the physical layer of ER.

Distributed Traffic Control Based on Road Network Partitioning Using Normalization Algorithm

With continuous economic development, most urban road networks are facing unprecedented traffic congestion. Centralized traffic control is difficult to achieve, and distributed traffic control based on partitioning a road network into subnetworks is a promising way to alleviate traffic pressure on urban roads. In order to study the differences between different partitioning methods chosen for distributed traffic control, we used the normalization algorithm to partition a part of the road network in Changsha City, and we used the results of the Girvan–Newman algorithm and the manual empirical partitioning method as a control group. Meanwhile, an abstract road network was constructed using VISSIM simulation software based on realistic road network parameters. And then, the different partitioning results were applied to the simulated road network to analyze the control effect. The results of the simulation software show that different partitioning methods have different effects on traffic control at subnetwork boundaries and improve traffic pressure to different degrees. Partitioning the road network into four subnetworks provided the greatest degree of traffic improvement. Overall, the proposed distributed traffic control method effectively improved operational efficiency and alleviated the traffic pressure of the road network.

Complex graph neural networks for medication interaction verification

This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain.

A comprehensive analysis of the hub genes for oxidative stress in ischemic stroke.

Ischemic stroke (IS), resulting from the occlusion of the cerebral artery and subsequent interruption of blood flow, represents a major and critical threat to public health. Oxidative stress (OS) has been confirmed to play a role in the IS pathological process and neural death. Understanding the essential role of OS-related genes in ischemic stroke is critical to understanding the current perception of the pathophysiological process in IS. Herein, by integrating three IS datasets (GSE16561, GSE22255, and GSE58294), we divided IS samples into the low- and high-OS groups by calculating the OS score identified by the oxidative stress gene set. The functional enrichment analysis of differentially expressed genes (DEGs) between the low- and high-OS groups indicated that DEGs were associated with hypoxia, the inflammatory response, and oxidative phosphorylation pathways. Furthermore, nine hub genes (namely TLR1, CXCL1, MMP9, TLR4, IL1R2, EGR1, FOS, CXCL10, and DUSP1) were identified through the Girvan-Newman algorithm and cytoHubba algorithms. Nine hub genes were highly expressed in IS samples and positively related to neutrophils and macrophages. Drug-sensitive analysis targeting hub genes defined allopurinol and nickel sulfate as potential candidates for impairing the neural death caused by oxidative stress in IS. Finally, we employed five machine learning methods to check the efficacy of the predictive model identified by nine hub genes. The results showed that our model had superior power for predicting the OS activity of IS patients. TLR4 was found to have excellent diagnostic value and a wide-spectrum interaction with other hub genes. Our research emphasized the impact of oxidative stress on ischemic stroke, which supports the idea that antioxidants hold great promise in ischemic stroke therapy.

Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions

Abstract Community detection theory is vital for the structural analysis of many types of complex networks, especially for human-like collaboration networks. In this work, we present a new community detection algorithm, the Targeted Community Merging algorithm, based on the well-known Girvan–Newman algorithm, which allows obtaining community partitions with high values of modularity and a small number of communities. We then perform an analysis and comparison between the departmental and community structure of scientific collaboration networks within the University of Zaragoza. Thus, we draw valuable conclusions from the inter- and intra-departmental collaboration structure that could be useful to take decisions on an eventual departmental restructuring.

Social media content classification and community detection using deep learning and graph analytics

Social cybermedia has greatly changed the world’s perspective on information sharing and propagation. These platforms build virtual user communities in the form of followings or followers that can spread positive or negative content. These virtual communities play a vital role in the spread of hate content, which may create a severe law-and-order situation and social unrest in any country. Hate content can include offensive, anti-state, anti-religion, sexist, and racist posts. Classifying such content and identifying influential individuals who participate in these communities are challenging tasks for law enforcement agencies. This study proposes novel deep learning and graph-based approaches to identifying hate content, followed by approaches to detecting communities and exploring social media to detect hate content. Twitter is used as a case study, and tweets are extracted and annotated by linguistic experts to develop a dataset for experimentation and validation. The proposed customized LSTM-GRU model is used to classify hate content into six categories. The developed model offers an accuracy of 98.14% on the obtained dataset. The Girvan–Newman algorithm is employed for Twitter community detection and successfully identifies the most influential individuals. Moreover, the proposed approach precisely detects intraclass communities and highly influential persons. The developed model effectively identifies hate tweets and communities and can be used to monitor social media to identify any potential threat of unrest.

Intentional controlled islanding based on dynamic community detection for power grid

Power system controlled islanding is an emergency control method used to stop the propagation of disturbances and avoid blackouts. Intentional controlled islanding (ICI) has been proposed as a corrective measure of last resort to split the power system into several sustainable islands. Complex network community detection is widely used in network partitioning and can be used to solve controlled islanding problems. The objective of this study is to apply the dynamic network community detection method to solve ICI problems. Using dynamic network modelling, the division-agglomeration (Di-Ag) algorithm is proposed. Dynamic modelling presets the computation task of the algorithm in the healthy grid phase. The algorithm balances the efficiency of the algorithm, power imbalance, and power flow disruption, without sacrificing other objectives to optimise one objective. Specifically, the grid topology is taken into account when the grid is divided using betweenness as edge weight. The first step of the Di-Ag algorithm uses the dynamic Girvan–Newman algorithm to achieve the goal of power imbalance. Moreover, only the islands' involved lines (nodes) are updated, which improves the efficiency of the algorithm. In the second step of the Di-Ag algorithm, the algorithm merges the islands to reduce power flow disruption. The IEEE 39-bus and 118-bus system are used to compare the performance of the proposed dynamic modelling method and the static network community discovery method in three different cases.

SOCIAL NETWORK ANALYSIS OF MANGOSTEEN TECHNOLOGY DEVELOPMENT CLUSTER IN INDONESIA BASED ON PATENT DOCUMENT APPLICATION

Functional food consumption is on the rise and has a significant market value. Indonesia is one of the largest mangosteens (a functional food source commodity) exporting countries globally. Unfortunately, the mangosteen export is still in fresh fruit condition, not in other forms that have a higher value. Policymakers need to identify critical technologies in the development of mangosteen commodities. This study uses a patent-based technology document analysis method to map the potential of technology. The data used is patent data that has been registered with the Indonesian Patent Office and the WIPO Patentscope database. The analysis was carried out using computational methods, namely a Social Network Analysis with the Girvan-Newman algorithm. According to the study's findings based on global patent data, there are three major technology clusters used in mangosteen patents: 1) 24 percent for technology related to developing preparations for medical, dental, or toilet purposes (A61K). 2) 20% for food and food ingredient technology or non-alcoholic beverages (A23L). The remaining 43 percent is spread across many other IPC technology codes, including technology related to drug preparations (A61P). It is in line with the results of patent data analysis in Indonesia, which also shows that there are three dominant technology groups applied to mangosteen in Indonesia, namely 1) Technology related to the development of medical, dental, and toilet technology (A61K) of 47 percent; 2) Technology related to food and food ingredients or non-alcoholic drinks (A23L) by 18 percent, and 3) Technology related to drug preparations (A61P) by 13 percent and the remaining 22 percent spread over several other IPC technology codes. According to Social Network Analysis, the world's dominant technology cluster for mangosteen is technology related to the development of food and food ingredients or non-alcoholic beverages (A23L). The technology associated with medical, dental, and toilet technology is the most important mangosteen technology cluster in Indonesia (A61K).

A Geometric-Based LSGDM Method for Tourism Project Decision Optimization with Trust-Distrust Relationships.

In this paper, we discuss the decision optimization of tourism projects in Hebei Province, China. To improve the process of analyzing tourism projects, we introduce a model that includes multiple decision makers as subjects based on a standard four-dimensional evaluation system. In order to improve the effectiveness of decision-making results, we will increase the number of decision makers to 40. A novel large-scale group decision-making (LSGDM) algorithm that incorporates the trust–distrust asymmetric relationships between decision makers is proposed. This model contains three main innovations: firstly, in the evaluation of decision makers’ social network relations, the trust–distrust value is introduced as a new carrier, and a weighted directed network and data integration operator are constructed based on the evaluation between decision makers; secondly, an extended Girvan-Newman (GN) algorithm is constructed to cluster the decision makers from this weighted network; thirdly, the interval-valued intuitionistic fuzzy number (IVIFN) is used to evaluate the alternatives, studying the IVIFN’s geometric significance by placing in a rectangular coordinate system. Finally, a new LSGDM model is proposed. Using the development of a cultural tourism project in a township as an example, the effectiveness of the proposed model is illustrated. By comparing the results of our method to those of a LSGDM algorithm that does not incorporate trust relationships, we assess the performance of the new model.

A Spark Parallel Betweenness Centrality Computation and its Application to Community Detection Problems

The Brandes algorithm has the lowest computational complexity for computing the betweenness centrality measures of all nodes or edges in a given graph. Its numerous applications make it one of the most used algorithms in social network analysis. In this work, we provide a parallel version of the algorithm implemented in Spark. The experimental results show that the parallel algorithm scales as the number of cores increases. Finally, we provide a version of the well-known community detection Girvan-Newman algorithm, based on the Spark version of Brandes algorithm.

Defense resource allocation in road dangerous goods transportation network: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach

A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach is proposed to allocate the defense resource in road dangerous goods transportation network. Firstly, the weighted physical network without direction and its weighted service network with direction are established. The Self-Contained Girvan-Newman Algorithm is applied to separate the whole weighted physical network into several communities. Next, based on the service network, the covariance matrix of each separated community is established, the Mean Variance Model is used to allocate the defense resource for each community, which focuses on selecting the option with the lowest probability of loss caused by the dangerous goods transportation accident/risk. The case study is conducted by using the road network and the dangerous goods transportation volume of Dalian, China as the background. When the whole network is separated into 6 communities, the defense resource capability in the whole network is the best. The Power Function Allocation Model (PFAM) is applied as the comparison approach, the overall rescue capability of the network is defined and applied to evaluate the defense resource allocation schemes. The results show that, the approach proposed in this paper has better effectiveness than PFAM, especially when the whole network is separated into 6 communities.

Community structure-aware fairness and goodness algorithm for link weight prediction

In this research, the problem of predicting the edge weight in the Bitcoin network has been resolved by utilizing the structure of the community based Fairness and Goodness. Community detection using Newman-Girvan algorithm has been applied to obtaine the trusted communities which depends on the features representation generally. The former may represent the trusted transactions that have trust value more that certain threshold. Concering the missing edge weight, the prediction is based on the fairness and goodness that supported by the above communities. In fairness measure, can capture how fair the node is in rating other nodes’ trust that has transactions (trusted transactions) with. While the goodness of a node shows how much this node is liked or trusted by other nodes that have transactions (trusted transactions) with. In both cases, only the nodes that are within community of the target node contribute with setting the fairness or the goodness of the target. The model was evaluated in practice using two real-world datasets; the Bitcoin-OTC and the Bitcoin-Alpha datasets. The experimental findings explain the effectiveness of the proposed comparable with other methods. The percentage error minimization is 18% for the Alpha dataset and 26% for OTC dataset.

Modularization of smart product service: A framework integrating smart product service blueprint and weighted complex network

Modularization of smart product service (SPS) emerges as a priority strategy to agilely satisfy the dynamic customization requirement and to flexibly response to the rapid market change. Compared with the traditional product service, the SPS have more complicated interactions between the service components due to the novel characteristics caused by the application of smart technologies. The SPS modularization presents great differences from the identification of service component, correlation evaluation and module partition with the traditional product service modularization. However, most the existing research mainly focuses on the context of traditional product service, while containing scant study of smart product service. Therefore, this study proposes a hybrid framework for SPS modularization. In the framework, a cyber-physical product service blueprint is firstly proposed to represent the SPS operation process and identify the SPS components. Then, a rough-fuzzy correlation matrix is presented to determine the comprehensive interdependence between all pairs of SPS components with fully considering the hybrid decision uncertainties involved in the evaluation process, i.e., intrapersonal linguistic vagueness and interpersonal preference diversity. After that, the complex network theory is used to construct the SPS network and a modified Girvan-Newman algorithm is adopted for the SPS module partition. Finally, an illustrative modularization case of smart gearbox maintenance service and some caparisons with other methods demonstrate the feasibility and validity of the proposed approach.

Hierarchical clustering supported by reciprocal nearest neighbors

Clustering is a fundamental tool aiming at classifying data points into groups based on their pairwise distances or similarities. It has found successful applications in all natural and social sciences, including biology, physics, economics, chemistry, astronomy, psychology, and so on. Among various types of algorithms, hierarchical clustering is of particular advantages as it can provide results under different resolutions without the knowledge of a predetermined number of clusters. At the same time, it is usually time-consuming or inaccurate. In this paper, we propose a novel hierarchical clustering algorithm on the basis of a simple hypothesis that two reciprocal nearest data points should be grouped in one cluster. Extensive tests on data sets across multiple domains show that our method is much faster and more accurate than the state-of-the-art benchmarks. We further extend our method to deal with the community detection problem in real networks, achieving remarkably better results than the well-known Girvan-Newman algorithm.