Deep learning-based community detection in complex networks with network partitioning and reduction of trainable parameters

Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions.

There is an increasing recognition on community detection in complex networks in recent years. In this study, we improve a recently proposed memetic algorithm for community detection in networks. By introducing a Population Generation via Label Propagation (PGLP) tactic, an Elitism Strategy (ES) and an Improved Simulated Annealing Combined Local Search (ISACLS) strategy, the improved memetic algorithm called (iMeme-Net) is put forward for solving community detection problems. Experiments on both computer-generated and real-world networks show the effectiveness and the multi-resolution ability of the proposed method.

The problem of community detection is relevant in many disciplines of science and modularity optimization is the widely accepted method for this purpose. It has recently been shown that this approach presents a resolution limit by which it is not possible to detect communities with sizes smaller than a threshold, which depends on the network size. Moreover, it might happen that the communities resulting from such an approach do not satisfy the usual qualitative definition of commune; i.e., nodes in a commune are more connected among themselves than to nodes outside the commune. In this paper we present a different method for community detection in complex networks. We define merit factors based on the weak and strong community definitions formulated by Radicchi [Proc. Natl. Acad. Sci. U.S.A. 101, 2658 (2004)] and we show that these local definitions avoid the resolution limit problem found in the modularity optimization approach.

Communities are basic units of complex networks and understanding of their structure help us to understand the structure of a network. Communities are groups of nodes that have many links inside and few links outside them. Community detection in a network can be modeled as an optimization problem. We can use some measures such as Modularity and Community Score for evaluating the quality of a partition of nodes. In this paper, we present a new algorithm for detecting communities in networks based on an Estimation of Distribution Algorithm (EDA) with the assumption that the problem variables are independent. EDAs are those evolutionary algorithms that build and sample the probabilistic models of selected solutions instead of using crossover and mutation operators. In this paper, we assess our algorithm by synthetic and real data sets and compare it with other community detection algorithms.

Community detection in complex networks has increasingly become an important topic in the network, but in most community detection methods, a single node only belongs to one community. In fact, there is often overlap among real-world online communities. In this paper, an overlapping community detection algorithm based on subgraph structure and multi-optimization method is designed. In this algorithm, the maximum clique mined by k-core decomposition is used as the clique node, thus the overlap characteristic is transformed into the inherent characteristic of the new graph. After that, a population initialization method based on k-core decomposition is designed, and the discrete framework of multi-objective particle swarm optimization algorithm is used to optimize the two objectives on the basis of maximal clique graph to solve the problem of overlapping community detection. In the real-world network, this algorithm is compared with similar community detection algorithms. The comparison of the evaluation indexe shows that the community detection effect of this algorithm is similar to that of similar algorithms, and has a good application prospect.

Communities are virtually ubiquitous in real-world networks, and the statistic of modularity index Q is the classical measurement for community detection algorithms. However, the relationship between the modularity property and network multilever micro-scale structures is still not clear. In this paper, we study community detection results both in artificial and real-life complex networks by constructing different order null networks, and the results uncover that how micro-structures (such as degree distribution, assortativity and clustering coefficient) affect community properties. Meanwhile, we also propose two novel null networks (increasing or decreasing community structures) to verify the robustness of different community detection algorithms. Our results indicate that the modularity index Q is not a suitable statistic to measure the weak community property which is widely available in empirical networks. Our findings can not only be used to test the robustness of different community detection methods, but also be helpful to uncover the correlation of network structures between microcosmic and mesoscopic scales.

A multi-objective adaptive evolutionary algorithm to extract communities in networks

Community detection in complex networks via adapted Kuramoto dynamics

Community detection in complex networks plays a pivotal role in identifying the structural composition of nodes, enabling us to pinpoint key topological features critical for a multitude of applications.Usually, the complex networks are in graphical format in which graph nodes denote objects and edges denote a connection between two things.The existing techniques have limitations such as early convergence and local optima issues.To overcome this issue, this manuscript proposed an Improved Hybrid Harris Hawk and Coot Bird Optimization (IHHHCBO) algorithm in community detection.The optimum separate movement of CBO is incorporated in HHO population initialization to strengthen both optimizations.It is employed to initialize the population for enhancing the position of diversity and change with other individuals.The Karate, Dolphin, Football and Political Books datasets are considered for assessing the IHHHCBO performance.The Ensemble Mutation Strategy (EMS) is developed to produce mutant candidate locations which enhances the exploration and population diversity of optimization.The Normalized Mutual Information (NMI), and Modularity (Q) are considered fitness functions in this research.The IHHHCBO performance is estimated through metrics like NMI, Q, f1-score and accuracy.The IHHHCBO reaches better accuracy of 1, 1, 0.998 and 0.883 for Karate, Dolphin, Football and Political Books respectively which is better when compared to existing algorithms like Core Structure Extraction Algorithm (CSEA), and Modified Crossover Opposition-based Genetic Algorithm (MCOBGA).

Multiplex community detection in complex networks using an evolutionary approach

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

Complex networks represent various real‐world systems. Overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real‐world known networks, including Zachary, Dolphins, and COVID‐19 Co‐authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state‐of‐the‐art overlapping community detection algorithms in terms of recall, precision, F‐score and overlapping normalized mutual information.

Community detection in complex networks using structural similarity

Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.