Find Community Structure Research Articles

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.

Discovery of habitat preferences and community structure of Echinoderms in Kri, Raja Ampat, Indonesia

Abstract. Siburian RHS, Tapilatu JR, Tapilatu ME. 2023. Discovery of habitat preferences and community structure of Echinoderms in Kri, Raja Ampat, Indonesia. Biodiversitas 24: 3968-3976. Raja Ampat, located in Papua, Indonesia, is home to one of the most diverse coral reef ecosystems and is a component of the Coral Triangle. The coral in Raja Ampat is still considered to be in good health. Although the marine fauna in Raja Ampat is pristine and diverse, there have still only been a few studies in this region. The scientific research on the Echinoderms in Raja Ampat is limited, particularly as a primary input for data research. In this study, we present Echinoderm's habitat and community structure in the Kri coastal area at Mansuar Island based on the data collected at field sampling. From January to February 2023, data were collected intensively during morning and afternoon low tide for three repetitions each, at 5-10 m depths. We conducted morphological species identification. Asteroidea, Holothuroidea, Echinoidea, and Crinoidea were found to be the four major classes of Echinodermata that dominated the sampling area, with a total of 20 species and 180 individuals. Each Echinodermata species has its preferred Kri coastal habitat based on its food supply, unique behavior, and body structure. In addition, the result indicates that this region has a moderate diversity level, with low dominance and moderate to high uniformity. This research provides new information on the ecology of Echinoderms in eastern Indonesia, particularly in Raja Ampat.

A nature-inspired algorithm to find community structure in complex networks

Complex networks have in generally communities. These communities are very important. Network’s communities represent sets of nodes, which are very connected. In this research, we developed a new method to find the community structure in networks. Our method is based on flower pollination algorithm (FPA) witch is used in the splitting process. The splitting of networks in our method maximizes a function of quality called modularity. We provide a general framework for implementing our new method to find community structure in networks. We present the effectiveness of our method by comparison with some known methods on computer-generated and real-world networks.

Community structure detection in networks based on Tabu search

Community structure is a main structural feature of networks, which can model many complex systems. It illustrates a lot of information about networks like their internal organisation and the degree of similarity between network nodes. Many methods have been proposed to find community structure in networks. However, there is always requirement to new methods for many reasons especially the accuracy. In this paper, we present a new method to find community structure in networks. Our method is hierarchical algorithm based on Tabu Search metaheuristic. A dendrogram is built by our method after dividing a network several times. The community structure will be selected from the built dendrogram based on quality function called Modularity. Our method has been tested on artificial benchmark and real networks. Results demonstrate the superiority of our method compared with several state-of-art methods.

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.

Combining multi-marker metabarcoding and digital holography to describe eukaryotic plankton across the Newfoundland Shelf

The planktonic diversity throughout the oceans is vital to ecosystem functioning and linked to environmental change. Plankton monitoring tools have advanced considerably with high-throughput in-situ digital cameras and genomic sequencing, opening new challenges for high-frequency observations of community composition, structure, and species discovery. Here, we combine multi-marker metabarcoding based on nuclear 18S (V4) and plastidial 16S (V4–V5) rRNA gene amplicons with a digital in-line holographic microscope to provide a synoptic diversity survey of eukaryotic plankton along the Newfoundland Shelf (Canada) during the winter transition phase of the North Atlantic bloom phenomenon. Metabarcoding revealed a rich eukaryotic diversity unidentifiable in the imaging samples, confirming the presence of ecologically important saprophytic protists which were unclassifiable in matching images, and detecting important groups unobserved or taxonomically unresolved during similar sequencing campaigns in the Northwest Atlantic Ocean. In turn, imaging analysis provided quantitative observations of widely prevalent plankton from every trophic level. Despite contrasting plankton compositions portrayed by each sampling method, both capture broad spatial differences between the northern and southern sectors of the Newfoundland Shelf and suggest complementary estimations of important features in eukaryotic assemblages. Future tasks will involve standardizing digital imaging and metabarcoding for wider use and consistent, comparable ocean observations.

The role of influential nodes and their influence domain in community detection: An approximate method for maximizing modularity

Community detection is one way to reduce the complexity of analyzing networks, especially with their rapid growth. Dividing networks into communities can help analysts and experts to understand the behavior and function of the networks. Also, besides the community structure, finding the influential nodes to spread information in the networks is a critical issue for researchers. Community detection is a challenging topic in network science and, various methods have been proposed for that. Many methods that find community structure use modularity as a measure to qualify the strength of community structure. These methods try to find community structures based on maximizing modularity. Modularity maximization is an NP-hard problem. One of the approaches that could solve such problems is approximate algorithms. Identifying the influential nodes which has many applications in complex networks can also be used in community detection. Therefore to maximize the modularity, in this paper, we first try to identify influential nodes, and then by estimating their influence domain, the communities are detected. We used scale-free networks concepts to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods in community detection algorithms. In addition, our proposed method also identifies the most influential node within each community.

Identifying robust functional modules using three-body correlations in Escherichia coli

Understanding the underlying structure of a gene regulatory network is crucial to understand the biological functions of genes or groups of genes. A common strategy to investigate it is to find community structure of these networks. However, methods of finding these communities are often sensitive to noise in the gene expression data and the inherent stochasticity of the community detection algorithms. Here we introduce an approach for identifying functional groups and their hierarchical organization in gene co-expression networks from expression data. A network describing the relatedness in the expression profiles of genes is first inferred using an information theoretic approach. Community structure within the inferred network is found by using modularity maximization. This community structure is further refined using three-body structural correlations to robustly identify important functional gene communities. We apply this approach to the expression data of E. coli genes and identify 25 robust groups, many of which show key associations with important biological functions as demonstrated by gene ontology term enrichment analysis. Thus, our approach makes specific and novel predictions about the function of these genes.

Community detection via network node vector label propagation

The discovery and analysis of community structure in complex networks is a hot topic in recent years. A community detection method called NVP is proposed based on the node vector label propagation rate of network nodes. The algorithm determines the centers by looking for NGC nodes, use the concept that the inner product of the node vector in the approximate modularity is greater than 0 to divide the initial community, and then divides the final community according to the relevant content of the label propagation rate. Experimental results show that the algorithm can effectively identify the community structure of various real-world networks and computer-generated networks. In addition, this algorithm can also obtain higher NMI values than DPC, FG, LE and DCN algorithms.

Community Detection in Partially Observable Social Networks

The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks’ topology and functions. However, most social network data are collected from partially observable networks with both missing nodes and edges . In this article, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong to multiple communities at once. To solve this problem, we introduce KroMFac , a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. Specifically, from an inferred Kronecker generative parameter matrix, we first estimate the missing part of the network. As our major contribution to the proposed framework, to improve community detection accuracy, we then characterize and select influential nodes (which tend to have high degrees) by ranking, and add them to the existing graph. Finally, we uncover the community structures by solving the regularized NMF-aided optimization problem in terms of maximizing the likelihood of the underlying graph. Furthermore, adopting normalized mutual information (NMI), we empirically show superiority of our KroMFac approach over two baseline schemes by using both synthetic and real-world networks.

Parallel label propagation algorithm based on weight and random walk.

Community detection is a complex and meaningful process, which plays an important role in studying the characteristics of complex networks. In recent years, the discovery and analysis of community structures in complex networks has attracted the attention of many scholars, and many community discovery algorithms have been proposed. Many existing algorithms are only suitable for small-scale data, not for large-scale data, so it is necessary to establish a stable and efficient label propagation algorithm to deal with massive data and complex social networks. In this paper, we propose a novel label propagation algorithm, called WRWPLPA (Parallel Label Propagation Algorithm based on Weight and Random Walk). WRWPLPA proposes a new similarity calculation method combining weights and random walks. It uses weights and similarities to update labels in the process of label propagation, improving the accuracy and stability of community detection. First, weight is calculated by combining the neighborhood index and the position index, and the weight is used to distinguish the importance of the nodes in the network. Then, use random walk strategy to describe the similarity between nodes, and the label of nodes are updated by combining the weight and similarity. Finally, parallel propagation is comprehensively proposed to utilize label probability efficiently. Experiment results on artificial network datasets and real network datasets show that our algorithm has improved accuracy and stability compared with other label propagation algorithms.

Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values.

Evolutionary Multi-Objective Optimization Algorithm for Community Detection in Complex Social Networks

Most optimization-based community detection approaches formulate the problem in a single- or bi-objective framework. In this paper, we propose two variants of a three-objective function optimization formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network. The first variant, named NSGA-III-KRM, considered Kernel k-means, ratio cut, and modularity, as three objective functions; whereas the second variant, named NSGA-III-CCM, considers community score, community fitness and modularity, as three objective functions. Experiments are conducted on four benchmark network datasets. Comparison with state-of-the-art and baseline methods along with decomposition-based multi-objective evolutionary algorithm variants (MOEA/D-KRM and MOEA/D-CCM) indicates that the proposed variants yield comparable or better results. This is particularly significant because the addition of the third objective does not worsen the results of the other two objectives. We also propose a simple method to rank the Pareto solutions obtained by proposing a new measure—the ratio of the hyper-volume and inverted generational distance. The higher the ratio, the better is the Pareto set. This strategy is particularly useful in the absence of empirical attainment function in the multi-objective framework, where the number of objectives is more than two.

Improved community structure discovery algorithm based on combined clique percolation method and K-means algorithm

Research on the community structure of networks is beneficial for understanding the structure of networks, analyzing their characteristics and discovering the rules hidden in these networks. To address issues from previous community mining algorithms, such as the low rate of convergence and high time complexity, this study proposes an improved community structure discovery algorithm named CPMK-Means algorithm. The main idea of this algorithm can be summarised as follows. The clique percolation method (CPM) algorithm generates the maximum number of cliques by combining depth-first search with breadth-first search so that the number of cluster centres is determined. Then, the k centres are selected based on the principle of the maximum degree of centres and minimum similarity between different centres. Afterwards, nodes in the network are assigned to the communities formed by the k centres, and the iterations are performed repeatedly until the centres become stable. Finally, the overlapping communities are merged. Experiments are carried out on standard data sets Football and Collins to evaluate the performance of the CPMK-Means algorithm. Results indicate that the CPMK-Means algorithm can achieve better community mining and higher execution efficiency compared with other algorithms. Furthermore, it is superior to other algorithms in terms of precision, recall, accuracy, F-measure and separation.

Improved community structure discovery algorithm based on penalised matrix decomposition for complex networks

Complex networks are one of the main research fields in data mining. In this study, a penalised matrix decomposition-based community structure discovery algorithm (PMDCSDA) for complex networks is proposed. The complex network is firstly transformed into an adjacency matrix, which is then processed for dimension reduction via principal component analysis. Numerous clusters are produced on the basis of penalised matrix decomposition. To evaluate the performance of the proposed PMDCSDA, we compare it with several classical algorithms, such as K-means, CPM and GN, using three complex network datasets. Experimental results demonstrate that the proposed algorithm can achieve improved performance in precision, recall, F1 and Sep indicator.

Leader-aware community detection in complex networks

Community structures are very common in complex networks. Detecting these communities is important for understanding the hidden features of networks. Besides, each community usually has one leader, which presents its significant influence over the whole community. However, most existing methods just focus on the problem of graph clustering, ignoring the role of community leaders. To solve this problem, in this paper, we propose a novel leader-aware community detection algorithm, which can find community structures as well as leaders of each community. This algorithm measures the leadership of each node and lets each one adhere to its local leader, forming dependence trees. Once all dependence trees are definitely settled, the community structures emerge because one tree actually is a cluster. Additionally, each root node of the tree is exactly the leader of corresponding community. This method can quickly determine the belonging of each node. Experimental results on real-world and benchmark networks demonstrate the effectiveness and the efficiency of our algorithm compared with other state-of-the-art approaches.

Parallel Heuristic Community Detection Method Based on Node Similarity

Community structure discovery can help us better understand the capabilities and functions of the network. However, many existing methods have failed to identify nodes in communities accurately. In this paper, we proposed a heuristic community detection method based on node similarities that are computed by assigning different edge weight influence factors based on different neighbor types of nodes. Concretely, by arbitrarily choosing a pair of nodes, we firstly found out the common neighbor nodes of the node pair and their corresponding neighbor nodes. Then, different edge weight influence factors are assigned according to the impact of different types of neighbor nodes on node similarity. Finally, the similarities between a pair of nodes are calculated by the proportion of various edge weight influence factors related to the node pair. Along the direction, a hash table based data storage and retrieval strategy with a lower conflict rate is introduced to hash the edge information into a ternary bucket structure that can be merged according to the same starting node. This operation can reduce the time complexity of the data query to a constant level, and realize the parallel computing of node similarity. When obtaining similarity of node pair, we merged nodes into communities by a heuristic hierarchical clustering. And, the resulting community structure is detected until all node similarities are calculated. With the help of the comparison tests of different methods based on the benchmark networks that have ground-truth communities, the proposed method for community detection provides better performance in both identification accuracy and time efficiency.

Efficient community detection method based on attribution of nodes in complex network

The research of network science is of great significance to the study of human society. The discovery of community structure in a network is an important research direction in the network science. Based on the research of traditional community discovery, this study found that sporadic nodes at the edge of a network only belong to the community to which the node is connected. Therefore, the community attributions of these sporadic edge nodes are stronger than that of the network central nodes. Based on this finding, the authors proposed a community discovery method based on the calculation of network node belongingness. In this proposed method, the community detection is first carried out based on the node attributions. Second, a simple method that determines the number of communities is defined. At last, the communities are optimally combined according to the average node attributions of the network so as to realise the community discovery of the network. This proposed algorithm has low time complexity and high detection accuracy in low coverage networks.

Fast detection of community structures using graph traversal in social networks

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real-time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in O(|V | + |E|) time to create an initial cover before using modularity maximization to get the final cover. Keywords - community detection; Influenced Neighbor Score; brokers; community nodes; communities

Comparative Study Of Complex Network Community Structure Algorithms In network Pharmacology Analysis

Community structure is an extremely important characteristic of complex networks composed of network pharmacology. The mining of network community structure is of great importance in many fields such as biology, computer science and sociology. In recent years, for different types of large-scale complex networks, researchers had proposed many algorithms for finding community structures. This paper reviewed some of the most representative algorithms in the field of network pharmacology, and focused on the analysis of the improved algorithms based on the modularity index and the new algorithms that could reflect the level and overlap of the community. Finally, a benchmark was established to measure the quality of the community classification algorithm.