DEMON

Community structure is an important feature of complex networks. These community structures have the fractal characteristics, that is, there is a self similarity of statistical sense between the complex networks and their local. There have been more and more recent researches on communities’ discovery in complex network. However, most existing approaches require the complete information of entire network, which is impractical for some networks, e.g. the dynamical network and the network that is too large to get the whole information. Therefore, the study of community discovery in complex networks has rather important theoretical and practical value. Through the analysis and study of the complex network evolution models with renormalization and the community change of the complex network evolution, using the tool of adjusting scales as the renormalization process, a multi-scale network community detection algorithm based on fractal feature evolution was proposed. The purpose is to solve community discovery problems in dynamic complex networks, and the effectiveness of the proposed method is verified by real data sets. By comparing result of this paper with the previous methods on some real world networks, and experimental results verify the feasibility and accuracy.

There have been more and more researches on community discovery in complex networks. Expanding of a source node into a community is one of the most successful methods for local community detection, especially when the global structure of the network is not accessible. In this paper, we propose CLOSE algorithm, Local Community Detection by LOcal Structure Expansion, based on the local expansion technique in the community detection. In CLOSE, we propose a novel connective function to identify a better source node. The node is in the center of a highly connected component of a graph. CLOSE selects a group of nodes instead of a single node to be the seed for the expansion of a local community. In addition, using the neighboring group can identify a suitable community for a hub node. Moreover, the expansion strategy is based on the label propagation technique instead of local community measurements. In experiments, we compare the performance of CLOSE with previous methods both on synthetic networks from the LFR Benchmark and real-world networks. We also examine the merit of the source node selection strategy. Both source node selection and community detection in CLOSE outperform previous algorithms.

Community discovery algorithm is one of the important topics in complex network research. However, there exist some problems in the traditional community discovery algorithm, such as the oscillation of label propagation, the convergence of iteration, the formation of a large community with a single label, namely “monster community”, or the poor effect of community discovery due to equal treatment of nodes. On the other hand, with the advent of the era of data, the computing power of single computer can't meet the demand of the rapid growth of complex network scale. Based on the above knowledge, this paper proposes the research and improvement of community discovery algorithm based on spark for large-scale complex networks. This paper first weights a complex network with no weight. Then, this paper chooses the classic efficient community discovery algorithm - label propagation algorithm to optimize label initialization, label propagation and label update strategy, iterative convergence strategy and so on, and establishes a new community discovery algorithm model. Then, the algorithm is connected to Spark, the algorithm is synchronized through GraphX programming, and a Spark experiment platform is established. Finally, some classic complex network data and some large-scale complex network data are tested and compared with some classic community discovery algorithms to verify the proposed algorithm is validated and verified by a large-scale complex network data set based on Spark GraphX platform does greatly improve the computational performance of community discovery in complex networks.

Nonnegative matrix factorization (NMF) has become a powerful model for community discovery in complex networks. Existing NMF-based methods for community discovery often factorize the corresponding adjacent matrix of complex networks to obtain its community indicator matrix. However, the adjacent matrix cannot represent the global structure feature of complex networks very well, and this leads to the performance degradation of community discovery. Besides, most of existing methods are not robust and scalable enough, so they are not effective to deal with complex networks with noises and large scales. Aiming at these problems above, in this paper we propose a method for community discovery using distributed robust NMF with SimRank similarity measure. This method selects SimRank measure to construct the feature matrix, which can more accurately represent the global structure feature of complex networks. To improve the robustness, we select $$\ell _{2,1}$$ norm instead of the widely used Frobenius norm to construct its NMF-based community discovery model. In addition, to improve the scalability, we implement its key components by using MapReduce distributed computing framework, including computing SimRank feature matrix and iteratively solving the NMF-based model for community discovery. We conduct extensive experiments on several typical complex networks. The results show that our method has better performance and robustness than other representative NMF-based methods for community discovery. Moreover, our method presents good scalability and hence can be used to discover communities in the large-scale complex networks.

Discovery of communities in networks is a fundamental data analysis problem. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links. Stochastic models provides a promising class of techniques for the identification of modular structures, but most stochastic models mainly focus on the detection of node communities rather than link communities. We propose a stochastic model, which not only describes the structure of link communities, but also considers the heterogeneous distribution of community sizes, a property which is often ignored by other models. We then learn the model parameters using a method of maximum likelihood based on an expectation-maximization algorithm. To deal with large complex real networks, we extend the method by a strategy of iterative bipartition. The extended method is not only efficient, but is also able to determine the number of communities for a given network. We test our approach on both synthetic benchmarks and real-world networks including an application to a large biological network, and also compare it with two existing methods. The results demonstrate the superior performance of our approach over the competing methods for detecting link communities.

Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities.

In view of that most of the current community discovery methods in social network do not consider node self-transfer and node bias, so that it is not possible to extract the graph features effectively, which leads to the ineffective problem by the community discovery, this paper proposes a novel social network community discovery algorithm (Local Distance Laplace, LDL). First, a Laplace matrix decomposition model is constructed based on the principle of matrix decomposition. Secondly, considering the high cost of global social network information acquisition and calculation, a community discovery model based on local distance is proposed. Finally, the optimal community structure is selected by using the NRO (Node Rank Optimization) function. A comprehensive comparative analysis is made on eleven real and synthetic networks. At the same time, validation analysis is conducted on eleven different social networks (Karate, Dolphins, Lemis, Public book, Football, Celegansnertal, Email, Public blogs, Netscience, Power, Hep_th). The experimental simulation results show that: in the real network, the proposed LDL algorithm improved the overall performance by nearly 7% compared with the seven state of art optimal methods (CoVeC, EDBC, JNMF, EADP, LPANNI, LSA, SCFS). The novel algorithm is reasonable and effective and it can also be extended to multi-scale community discovery.

To detect groups in networks is an interesting problem with applications in social and security analysis. Many large networks lack a global community organization. In these cases, traditional partitioning algorithms fail to detect a hidden modular structure, assuming a global modular organization. We define a prototype for a simple local-first approach to community discovery, namely the democratic vote of each node for the communities in its ego neighborhood. We create a preliminary test of this intuition against the state-of-the-art community discovery methods, and find that our new method outperforms them in the quality of the obtained groups, evaluated using metadata of two real world networks. We give also the intuition of the incremental nature and the limited time complexity of the proposed algorithm.

Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.

Community structure is an important feature of a complex network, where detection of the community structure can shed some light on the properties of such a complex network. Amongst the proposed community detection methods, the label propagation algorithm (LPA) emerges as an effective detection method due to its time efficiency. Despite this advantage in computational time, the performance of LPA is affected by randomness in the algorithm. A modified LPA, called CLPA-GNR, was proposed recently and it succeeded in handling the randomness issues in the LPA. However, it did not remove the tendency for trivial detection in networks with a weak community structure. In this paper, an improved CLPA-GNR is therefore proposed. In the new algorithm, the unassigned and assigned nodes are updated synchronously while the assigned nodes are updated asynchronously. A similarity score, based on the Sørensen-Dice index, is implemented to detect the initial communities and for breaking ties during the propagation process. Constraints are utilised during the label propagation and community merging processes. The performance of the proposed algorithm is evaluated on various benchmark and real-world networks. We find that it is able to avoid trivial detection while showing substantial improvement in the quality of detection.

Community discovery in complex networks has become a core issue in multidisciplinary cross-disciplinary research and has been successfully applied in many areas, such as social network analysis, protein network analysis, and link prediction. This paper proposes a genetic algorithm based on adaptive cross mutation operator for complex network community mining. The population is generated by establishing fitness value calibration and adaptive cross mutation operator, and a good individual is selected from it. An improved adaptive cross mutation operator is proposed to ensure the convergence of genetic algorithm and accelerate the generation of optimal solution while maintaining the diversity of population. Finally, experiments were carried out in multiple real networks to verify the stability and efficiency of the algorithm.

Identifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis.This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

Automated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the task of grouping similar objects and segregating dissimilar objects. The community detection problem can be thought of as finding groups of densely interconnected nodes with few connections to nodes outside the group. A node similarity measure is proposed here that finds the similarity between two nodes by considering both neighbors and non-neighbors of these two nodes. Subsequently, a method is introduced for identifying communities in complex networks using this node similarity measure and the notion of data clustering. The significant characteristic of the proposed method is that it does not need any prior knowledge about the actual communities of a network. Extensive experiments on several real world and artificial networks with known ground-truth communities are reported. The proposed method is compared with various state of the art community detection algorithms by using several criteria, viz. normalized mutual information, f-measure etc. Moreover, it has been successfully applied in improving the effectiveness of a recommender system which is rapidly becoming a crucial tool in e-commerce applications. The empirical results suggest that the proposed technique has the potential to improve the performance of a recommender system and hence it may be useful for other e-commerce applications.

Finding communities in networks is one of the challenging issues in complex network research. We have to deal with very large networks that contain billions of vertices, which makes community discovery a computationally intensive work. Moreover, communities usually overlap each other, which greatly increases the difficulty of identifying the boundaries of communities. In this paper, we propose a parallel multi-label propagation algorithm (PMLPA) that enhances traditional multi-label propagation algorithm (MLPA) in two ways. First, the critical steps of MLPA are parallelized based on the MapReduce model to get higher scalability. Second, new label updating strategy is used to automatically determine the most valuable labels of each vertex. Furthermore, we study the improvement of PMLPA through considering the influence of vertices and labels on label updating. In this way, the importance of each label can be described with higher precision. Experiments on artificial and real networks prove that the proposed algorithms can achieve both high discovering accuracy and high scalability.

In recent years, the discovery of local communities in multi-layer networks has become an active research field of complex systems. Such as communication, social networking, sensor network the rapid development of new technologies, the amount of data generated by increased, all want to obtain the network information difficulty is big, and the network, community, mutual influence between nodes, greatly enhance the complexity of network and make local found existing multilayer network method is unable to get more accurate test results. In this paper, based on the homogeneity drive of multi-layer network and the influence relation index of multi-layer path length measurement, a local community detection model based on the influence relation of the multi-layer network is proposed by combining the direct influence relation and indirect influence relation of the network (IMLC). Compared with six real multi-layer network data sets, the algorithm has better robustness in many most advanced multi-layer methods: GL, PMM, and ML-LCD.