Parallel Multi-Label Propagation Based on Influence Model and Its Application to Overlapping Community Discovery

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.

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.

The objective of this study is to discover social communities in a social network using different social network community discovery methods that utilize metrics and structures like degree, clustering coefficient, k-cores, weak and strong components. We have used two different datasets and methods: K-core community discovery method for DBLP dataset and Main Path Analysis method for Arxiv High-energy physics theory citation network. At the end of the analyses, we have obtained several reports that represent the skeleton structure of the communities in the networks.

Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.

This chapter explores the topic of virtual community discovery in social networks. It starts with defining virtual communities. After that, the chapter goes over several network modelling strategies and community identification techniques. The significance of standardised datasets in evaluating algorithm performance is shown by a thorough analysis of benchmark networks used to test community detection techniques. The accuracy indices for assessing virtual community identification algorithms are also covered. The chapter offers common datasets needed to evaluate virtual community discovery techniques. Next, it explores community discovery using the Girvan-Newman and Louvain algorithms, two well-known techniques, to demonstrate practical applications. These illustrations explain how theoretical ideas can be applied in practical settings. Finally, this chapter provides a thorough overview of the concepts and assessment criteria for virtual community discovery in social networks, providing insightful information for both scholars and industry professionals.

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.

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.

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.

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

Community structure is a fundamental feature for many networks. The problem of discovering communities in those networks thus has been attracting a lot of research. However, due to the rapid increase of networks’ scale and the availability of real communities in many networks, the task of detecting communities in large real networks remains a challenging problem. In this paper, we study the structure of various large real networks and their ground-truth community structures and observe an interesting phenomenon: the difference of degrees (abbreviated as dod) of connected nodes follows a heavy-tailed distribution with an approximate power-law tail for large dod, in both original network and community structure but to different extents. With the aim to explore the effect of this observation on identifying communities in real large networks, we propose a weighting strategy and further embed it into two prominent community detection algorithms. Comparisons against the state of the arts demonstrate a very promising performance of the proposed weighting strategy.

Label propagation based evolutionary clustering for detecting overlapping and non-overlapping communities in dynamic networks

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.

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.

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.

The distribution of user community in social network has the problem of similar feature distribution, which leads to the high viscosity between community and community, and it is difficult to mine community features. A social network community discovery algorithm is proposed based on parallel recommendation. The fuzzy decision model of social network community discovery is established by taking the user behavior value, user consumption value and loyalty of social network as independent variables. The relationship between the interaction degree and the recommendation effect in the social network is analyzed by extracting the characteristic quantity of the community association attribute in the social network, and the community group recommendation in the social network is carried out by using the parallel recommendation algorithm, and the number of visits between users is analyzed. The number of messages is used as the weighted weight coefficient, the difference factor is introduced to evaluate the influence of the community, the synchronous label of the social network community is established, and the community feature mining and parallel recommendation are realized according to the label location. The simulation results show that, the proposed method has good accuracy in community discovery, high accuracy in community attribute feature mining, and low cost of the algorithm. It has obvious advantages compared with similar algorithms.