Communities In Complex Networks Research Articles

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

A complex network is a method to analyze nonlinear time series on multiple temporal and spatial scales. K-core analysis of several typical nonlinear series was studied, and then based on this, a fast algorithm method was employed to divide the community structure. According to the definition of k-core, the rolling bearings complex network was divided into different cores at the router level, and the main characteristic quantities such as degree distribution and clustering coefficient of every k-core can be analyzed. The results show that there is a corresponding relationship between the characteristic parameters and the community structure of the bearing. The analysis results show that compared with the traditional method, the method of complex network community structure and k-core analysis has a better diagnosis effect.

Epidemic Model-based Network Influential Node Ranking Methods: A Ranking Rationality Perspective

Existing surveys and reviews on Influential Node Ranking Methods (INRMs) have primarily focused on technical details, neglecting thorough research on verifying the actual influence of these nodes in a network. This oversight may result in erroneous rankings. In this survey, we address this gap by conducting an extensive analysis of 82 primary studies related to INRMs based on the epidemic model over the past 20 years. We statistically analyze and categorize benchmark networks into four types, and conclude that high-quality networks with complete information are insufficient and most INRMs only focus on undirected and unweighted networks, which encourages collaboration between industry and academia to provide optimized networks. Additionally, we categorize and discuss the strengths, weaknesses, and optimized crafts of seven categories of INRMs, helping engineers and researchers narrow down their choices when selecting appropriate INRMs for their specific needs. Furthermore, we define the Capability and Correctness metrics and utilize their usage frequency and functionality to assist engineers and researchers in prioritizing and selecting suitable metrics for different INRMs and networks. To our knowledge, this is the first survey that systematically summarizes the Capability and Correctness of INRMs, providing insights for the complex network community and aiding INRM selection and evaluation.

A novel intelligent Fuzzy-AHP based evolutionary algorithm for detecting communities in complex networks

A novel intelligent Fuzzy-AHP based evolutionary algorithm for detecting communities in complex networks

Cover Image

The cover image is based on the Original Article Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm by Lei Pang et al., https://doi.org/10.1049/syb2.12083image

Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm.

We analyzed the symptoms composition of Interstitial Cystitis (IC), the regularity of the evolution of symptoms before and after treatment, and the visualization of the community network, to provide a reference for clinical diagnosis and treatment of Interstitial Cystitis. Based on the outpatient electronic case data of 552 patients with Interstitial Cystitis, we used a complex network community discovery algorithm, directed weighted complex network, and Sankey map to mine the data of the symptoms composition of Interstitial Cystitis, the evolution of symptoms before and after treatment and the visualization of the community network, to analyze the epidemiological characteristics of interstitial cystitis symptoms in the real world. By the community division of the complex network of interstitial cystitis symptoms, We finally obtained three core symptom communities. Among them, symptom community A (bladder-related symptoms) is the symptom community with the highest proportion of nodes (60.00%) in the complex network of Interstitial Cystitis, symptom community B (non-bladder-related symptoms 1) ranks second (32.00%) in a complex network of Interstitial Cystitis, and symptom community C (non-bladder-related symptoms 2) has the lowest proportion (8.00%). There is a complex evolutionary relationship between the symptoms of Interstitial Cystitis before and after treatment. Among the single symptoms before and after treatment, the decreased rate of Day frequency is 93.22%, and the reduced urgency rate is 93.07%. The decline rate of Nocturia was 82.33%. From the perspective of different communities, theoverall symptoms of symptom community A decreased by 34.39% after treatment, the general symptoms of symptom community B decreased by 35.37%, and the prevalent symptoms of symptom community C decreased by 71.43%. In the case of using diet regulation treatment to treat bladder pain, the cure rate of bladder pain can reach 22.67%. The cure rate of burning in bladders can get 15.38% with Percutaneous Sacral neuromodulation, 96.95% with diet regulation treatment, and 100% with Percutaneous Sacral neuromodulation. When using behavioral physiotherapy to treat bladder pain, 3.57% of the patient's symptoms change to bladder discomfort; 4% of the patient's symptoms change to bladder discomfort when using oral medicine to treat bladder pain.Symptom research methods based on community findings can effectively explore the rule of symptom outcome of Interstitial Cystitis before and after treatment, and the results are highly interpretable by professionals. The cover image is based on the Original Article Research on symptoms composition, time series evolution, and network visualisation of interstitial cystitis based on complex network community discovery algorithm by Lei Pang etal., https://doi.org/10.1049/syb2.12083.

Federated Clique Percolation for Privacy-preserving Overlapping Community Detection

Community structure is a typical characteristic of complex networks. Finding communities in complex networks has many important applications, such as the advertisement and recommendation based on social networks and the discovery of new protein molecules in biological networks, which make it a hot topic in the field of complex network analysis. With the increasing concerns about the leakage of personal privacy, discovering communities spread across the local networks owned by multiple participants accurately while preserving each participant’s privacy has become an emerging challenge in distributed community detection. In this article, we propose a general federated graph learning model for privacy-preserving distributed graph learning and develop two federated clique percolation algorithms (CPAs) based on it to discover overlapping communities distributed across multiple participants’ local networks without disclosing any participant’s network privacy. Homomorphic encryption and hash operation are used in combination to protect the privacy of the vertices and edges of each local network. Furthermore, vertex attributes are involved in the calculation of clique similarity and clique percolation when dealing with attributed networks. The experimental results on real-world and artificial datasets demonstrate that the proposed algorithms achieve identical results to those of their stand-alone counterparts and more than 200% higher accuracy than the simple distributed CPAs without federating learning.

A new approach for estimating the number of communities in complex networks using PGD-SNMTF and GA

A new approach for estimating the number of communities in complex networks using PGD-SNMTF and GA

Machine learning to set hyperparameters for overlapping community detection algorithms

AbstractThe Local Fitness Method (LFM) and Speaker‐Listener Label Propagation (SLPA) algorithms are widely used to detect overlapping communities in complex networks. The main problem with these two algorithms is that they are extremely sensitive to the setting of the hyperparameters. Previous methods set the hyperparameters of LFM or SLPA based on either empirical values or random choices, resulting in a large amount of computation for adjusting those parameters. To solve this problem, in this paper, a machine‐learning‐based approach is proposed to automatically set the hyperparameters of these two algorithms. Experimental results show that compared with the manual method, automatically setting the hyperparameter using machine learning models can lead to higher‐quality divisions. Furthermore, in comparison to the well‐known hyperparameter tuning method using Bayesian Optimization, the proposed predictive model‐based approach can find suitable para meters for LFM and SLPA much faster, while achieving competitive results in terms of division quality.

GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

Local overlapping community detection is a hot problem in the field of studying complex networks. It is the process of finding dense clusters based on local network information. This paper proposes a method called local greedy extended dynamic overlapping community detection (GLOD) to address the challenges of detecting high-quality overlapping communities in complex networks. The goal is to improve the accuracy of community detection by considering the dynamic nature of community boundaries and leveraging local network information. The GLOD method consists of several steps. First, a coupling seed is constructed by selecting nodes from blank communities (i.e., nodes not assigned to any community) and their similar neighboring nodes. This seed serves as the starting point for community detection. Next, the seed boundaries are extended by applying multiple community fitness functions. These fitness functions determine the likelihood of nodes belonging to a specific community based on various local network properties. By iteratively expanding the seed boundaries, communities with higher density and better internal structure are formed. Finally, the overlapping communities are merged using an improved version of the Jaccard coefficient, which is a measure of similarity between sets. This step ensures that overlapping nodes between communities are properly identified and accounted for in the final community structure. The proposed method is evaluated using real networks and three sets of LFR (Lancichinetti–Fortunato–Radicchi) networks, which are synthetic benchmark networks widely used in community detection research. The experimental results demonstrate that GLOD outperforms existing algorithms and achieves a 2.1% improvement in the F-score, a community quality evaluation metric, compared to the LOCD framework. It outperforms the best existing LOCD algorithm on the real provenance network. In summary, the GLOD method aims to overcome the limitations of existing community detection algorithms by incorporating local network information, considering overlapping communities, and dynamically adjusting community boundaries. The experimental results suggest that GLOD is effective in improving the quality of community detection in complex networks.

Detecting local communities in complex network via the optimization of interaction relationship between node and community.

The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.

Communities Detection in Multiplex Networks Using Optimization: Study Case—Employment in Mexico during the COVID-19 Pandemic

The detection of communities in complex networks offers important information about the structure of the network as well as its dynamics. However, it is not an easy problem to solve. This work presents a methodology based of the robust coloring problem (RCP) and the vertex cover problem (VCP) to find communities in multiplex networks. For this, we consider the RCP idea of having a partial detection based onf the similarity of connected and unconnected nodes. On the other hand, with the idea of the VCP, we manage to minimize the number of groups, which allows us to identify the communities well. To apply this methodology, we present the dynamic characterization of job loss, change, and acquisition behavior for the Mexican population before and during the COVID-19 pandemic modeled as a 4- layer multiplex network. The results obtained when applied to test and study case networks show that this methodology can classify elements with similar characteristics and can find their communities. Therefore, our proposed methodology can be used as a new mechanism to identify communities, regardless of the topology or whether it is a monoplex or multiplex network.

Overlapping Community Detection Algorithm Based on High-Quality Subgraph Extension in Local Core Regions of Network

Community structure is an important feature of complex networks. Detecting overlapping communities in complex networks is a hot research topic in data mining and graph theory, aiming at the shortcomings of community detection algorithm based on seed expansion, such as the instability of community detection results caused by randomly selecting seeds, the similarity of selected seeds leading to similar communities after different seed expansion, and the increase of calculation caused by deleting nodes in the process of seed expansion. This paper proposes an overlapping community detection algorithm based on high-quality subgraph extension in local core regions of the network (OLCRE). First, a novel seed community selection method is designed. By analyzing the sum of node degrees of the subgraph formed by a node and its neighbor nodes in the local core region of the network and the tightness of the internal and external connections of the subgraph, a seed community selection function is proposed. According to this function, high-quality subgraphs are selected from all the local core regions of the network as seed communities. Then, taking the definition of community as the guideline, a new community expansion strategy is proposed. Considering the influence of the neighbor node on the inner and outer connection tightness of the seed community comprehensively, it is determined whether the neighbor node can join the seed community. Finally, after the completion of all seed community expansion, overlapping nodes and possible missing nodes should be simplified and redetected to further improve the quality of community detection. The proposed algorithm is tested on the artificial and real-world networks and compared with several overlapping community detection algorithms. The experimental results verify the effectiveness and feasibility of the proposed algorithm.

Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based Encoding

Detecting communities in complex networks can shed light on the essential characteristics and functions of the modeled phenomena. This topic has attracted researchers from both academia and industry. Among different community detection methods, genetic algorithms (GAs) have become popular. Considering the drawbacks of the currently used locus-based and solution-vector-based encodings to represent the individuals, in this article, we propose (1) a new node similarity-based encoding method, MST-based encoding, to represent a network partition as an individual, which can avoid the shortcomings of the previous encoding schemes. Then, we propose (2) a new adaptive genetic algorithm for the purpose of detecting communities in networks, along with (3) a new initial population generation function to improve the convergence time of the algorithm, and (4) a new sine-based adaptive mutation function which adjusts the mutations according to the improvement in the fitness value of the best individual in the population pool. The proposed method combines the similarity-based and modularity-optimization-based approaches to find communities in complex networks in an evolutionary framework. Besides the fact that the proposed encoding can avoid meaningless mutations or disconnected communities, we show that the new initial population generation function and the new adaptive mutation function can improve the convergence time of the algorithm. Several experiments and comparisons were conducted to verify the effectiveness of the proposed method using modularity and NMI measures on both real-world and synthetic datasets. The results show that the proposed method can find the communities in a significantly shorter time than other GAs while reaching a better trade-off in the different measures.

Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks.

Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties.

Detecting Overlapping Communities in Complex Networks: An Evolutionary Label Propagation Approach

A challenging issue in complex network analysis is overlapping community detection, which has attracted many studies. Label Propagation Algorithm (LPA) is one of the famous studies to detect communities. But it has some weaknesses such as using local information and randomly choosing the sequences of processing nodes. We introduce Evolutionary Label Propagation Algorithm (ELPA) to solve these problems and improve accuracy. ELPA uses an intelligent search instead of randomly processing nodes and fuses local and global perspectives. The proposed ELPA is compared with several state-of-the-art algorithms on synthetic and real-world networks with different sizes, densities, and complexities. The results indicate that ELPA provides better results on most of the test instances. Therefore, ELPA is an accurate and efficient algorithm for detecting overlapping communities.

Detecting communities in complex networks using triangles and modularity density

The community structure is an important property of complex networks. Community detection of complex networks can be used to help reveal the relationship between topology and functionality. Modularity density QD, as a community quality function, has been widely used for detection of community structure in networks. Here, we develop a novel modularity density based on triangular motifs, and also prove its equivalence with the spectral clustering and non-negative matrix factorization. In addition, we develop a community detection method that incorporates the edge and triangular motif information, and apply the proposed method to several classical complex networks. Experiments show that the proposed method outperforms the standard modularity density QD.

A novel complex network community clustering method for fault diagnosis

The complex network, as a method for the analysis of nonlinear and non-stationary signals, overcomes the shortcomings of traditional time-frequency analysis methods and proves its effectiveness in mechanical fault diagnosis. Community clustering, a type of complex network, has made great progress in recent years. However, the existing community clustering algorithms have disadvantages in that they lack significant global extreme value and huge search spaces. Therefore, a Fast Newman algorithm based on reliability judgment is proposed. Starting from the community structure characteristics of the complex network, with the fault sample as a network node, the relationship between the samples as a connected edge and a complex network model of fault data is established. Clusters in troubleshooting are transformed into community structure discovery in the network. Firstly, the initial division of the community is obtained by measuring the distance between the samples. Then, the modularity index of the network is used as a standard function of the community division, and the bottom-up community merger is performed. The local edge density index is used for reliability determination before each combination to achieve global optimization, and the network block structure is the most obvious. Finally, with all the data merged into one community, the optimal division of the community structure is obtained, while accurate community clustering and fault diagnosis is realized. The benchmark graphs for testing community detection (Lancichinetti–Fortunato–Radicchi benchmark standard test network, LFR) and different fault data of rolling bearings under multiple operating conditions are applied to verify the effectiveness of this method; the results prove that the modified Fast Newman algorithm has better clustering effects and a higher accuracy rate than the original algorithm. Compared with K-means clustering and fuzzy clustering, the modified Fast Newman algorithm achieves higher performance in fault diagnosis of rolling bearings under multiple operating conditions.

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.

Detecting overlapping communities in complex networks using non-cooperative games

Detecting communities in complex networks is of paramount importance, and its wide range of real-life applications in various areas has caused a lot of attention to be paid to it, and many efforts have been made to have efficient and accurate algorithms for this purpose. In this paper, we proposed a non-cooperative game theoretic-based algorithm that is able to detect overlapping communities. In this algorithm, nodes are regarded as players, and communities are assumed to be groups of players with similar strategies. Our two-phase algorithm detects communities and the overlapping nodes in separate phases that, while increasing the accuracy, especially in detecting overlapping nodes, brings about higher algorithm speed. Moreover, there is no need for setting parameters regarding the size or number of communities, and the absence of any stochastic process caused this algorithm to be stable. By appropriately adjusting stop criteria, our algorithm can be categorized among those with linear time complexity, making it highly scalable for large networks. Experiments on synthetic and real-world networks demonstrate our algorithm’s good performance compared to similar algorithms in terms of detected overlapping nodes, detected communities size distribution, modularity, and normalized mutual information.

A Software Vulnerability Detection Method Based on Complex Network Community

To find out whether there is any vulnerability in software programs where conditional judgment is ignored, this article proposes a software vulnerability detection method based on complex network community. First, the method abstracts the software system into a directed weighted graph by using the software algebraic component model and then preprocesses the directed weighted graph to get a complex network graph. Then, by using the partition algorithm, the complex network graph is divided into the communities, and the key nodes in communities are found by nRank algorithm. Finally, the graph of the key nodes with high influence is matched with the complex network graph that has been preprocessed. In order to evaluate the effectiveness of the community partition algorithm and the nRank algorithm, comparative experiments are carried out on two datasets. The experimental results show that the community partition algorithm is better than the comparison algorithm in precision, recall, and comprehensive evaluation index, and the nRank algorithm is closer to the result of degree centrality measurement index than the PageRank algorithm and the LeaderRank algorithm. The spring-shiro-training project is used to verify the vulnerability detection method based on complex network community, and the results show that the method is effective.