Community Detection In Dynamic Networks Research Articles

An adaptive dynamic community detection algorithm based on multi-objective evolutionary clustering

PurposeCommunity detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.Design/methodology/approachThis paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.FindingsExperimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.Originality/valueTo enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

Local Community Detection in Graph Streams with Anchors

Community detection in dynamic networks is a challenging research problem. One of the main obstacles is the stability issues that arise during the evolution of communities. In dynamic networks, new communities may emerge and existing communities may disappear, grow, or shrink. As a result, a community can evolve into a completely different one, making it difficult to track its evolution (this is known as the drifting/identity problem). In this paper, we focused on the evolution of a single community. Our aim was to identify the community that contains a particularly important node, called the anchor, and to track its evolution over time. In this way, we circumvented the identity problem by allowing the anchor to define the core of the relevant community. We proposed a framework that tracks the evolution of the community defined by the anchor and verified its efficiency and effectiveness through experimental evaluation.

An enforced non-negative matrix factorization based approach towards community detection in dynamic networks

Identifying community structures within network dynamics is important for analysing the latent structure of the network, understanding the functions of the network, predicting the evolution of the network as well as detecting unusual events of the network. From various perspectives, a diversity of approaches towards dynamic community detection has been advised. However, owing to the difficulty in parameter adjustment, high temporal complexity and detection accuracy is diminishing as time slice rises; and recognizing the community composition in dynamic networks gets extremely complex. The basic models, principles, qualities, and techniques of latent factor models, as well as their various modifications, generalizations and extensions, are summed up systematically in this study which focuses on both theoretical and experimental research into latent factor models across the latest ten years. Latent factor model like non-negative matrix factorization is considered one of the most successful models for community identification which aims to uncover distributed lower dimension representation so as to reveal community node membership. These models are mostly centred on reconstructing the network from node representations while requiring the representation to have special desirable qualities (non-negativity). The purpose of this work is to provide an experimental as well as theoretical comparative analysis of the latent factor approaches employed to detect communities within dynamic networks. Parallelly we have devised the generic and improved non-negative matrix factorization-based model which will help in producing robust community detection results in dynamic networks. The results have been calculated from the experiments done in Python. Moreover our models methodology focuses on information dynamics so as to quantify the information propagation among the involved nodes unlike existing methods that considers networks first-order topological information described by its adjacency matrix without considering the information propagation between the nodes. In addition, this paper intends to create a unified, state of the art framework meant for non-negative matrix factorization conception which could be useful for future study.

Dynamic community detection based on the Matthew effect

The identification of community structures plays a crucial role in analyzing network topology, exploring network functions, and mining potential patterns in complex networks. Many algorithms have been proposed for identifying community structures in static networks from different perspectives. However, most networks in the real world are not static and their structures constantly evolve over time. Identifying community structures in dynamic networks remains a challenging task because of the variability, complexity, and large scale of dynamic networks. In this study, we propose a framework and Matthew effect model for community detection in dynamic networks. Based on this architecture and model, we design a dynamic community detection algorithm called, Dynamic Community Detection based on the Matthew effect (DCDME), which employs a batch processing method to reveal communities incrementally in each network snapshot. DCDME has several desirable benefits: high-quality community detection, parameter-free operation, and good scalability. Extensive experiments on synthetic and real-world dynamic networks have demonstrated that DCDME has many advantages and outperforms several state-of-the-art algorithms.

Research on Dynamic Community Detection Method Based on an Improved Pity Beetle Algorithm

In the last decade, community detection in dynamic networks has received increasing attention, because it can not only uncover the community structure of the network at any time but also reveal the regularity of dynamic networks evolution. Although methods based on the framework of evolutionary clustering are promising for dynamic community detection, there is still room for further improvement in the snapshot quality and the temporal cost. In this study, a dynamic community detection algorithm based on optional pathway guide pity beetle algorithm (DYN-OPGPBA), which is a novel dynamic community detection method based on the framework of evolutionary clustering, is proposed. We propose an improved PBA for community detection of the network at the first time step, including a discrete search strategy based on adjacent nodes, a closeness-based community modification strategy and a crowded community split strategy. Compared with many representative static community detection methods, the proposed method has some superior detection accuracy. A neighbour vector competition-based individual update strategy and an external population size restriction mechanism are also proposed for community detection at subsequent time steps. Results show that DYN-OPGPBA has a better balance between snapshot quality and temporal cost than two representative dynamic community detection methods.

Incremental methods for community detection in both fully and growing dynamic networks

Abstract In recent years, community detection in dynamic networks has received great interest. Due to its importance, many surveys have been suggested. In these surveys, the authors present and detail a number of methods that identify a community without taking into account the incremental methods which, in turn, also take an important place in dynamic community detection methods. In this survey, we provide a review of incremental approaches to community detection in both fully and growing dynamic networks. To do this, we have classified the methods according to the type of network. For each type of network, we describe three main approaches: the first one is based on modularity optimization; the second is based on density; finally, the third is based on label propagation. For each method, we list the studies available in the literature and state their drawbacks and advantages.

A signal processing perspective to community detection in dynamic networks

Many real world systems ranging from biological to social systems can be modeled as networks. As network data becomes more ubiquitous, it is important to reduce the large-scale networks into smaller modules or communities. Most of the original research in community detection has focused on static networks while many real world networks such as social and biological networks exhibit varying topology over time. For this reason, over the past twenty years various community detection methods for dynamic networks have been developed in physics, statistics, computer science and signal processing. In this paper, we present an overview of the current state-of-the-art in community detection for dynamic networks and illustrate some of the shortcomings of existing methods. We also outline future challenges and opportunities in this research area.

Community detection in dynamic networks: a comprehensive and comparative review using external and internal criteria

Community detection in dynamic networks: a comprehensive and comparative review using external and internal criteria

A spiderweb model for community detection in dynamic networks

Community detection in dynamic networks is one of the most challenging tasks in the field of network analysis. In general, networks often evolve smoothly between successive snapshots. Therefore, the community structure detected in each snapshot should not only be of high quality but also reflect the smoothness of the variations compared with the previous snapshot. In this paper, we propose a novel incremental community-detection method named Spiderweb, which detects the community structure in each snapshot by simulating the evolution of spiderwebs. We categorize the evolutionary events of the network into three types, and then address the changed nodes and edges according to three corresponding evolution rules. In this procedure, some nodes are assigned to proper communities. Then, we construct a new subgraph for the unclassified changed nodes, and detect its communities efficiently. Finally, we merge some communities to obtain the resulting community structure. We conduct extensive experiments on both artificial networks and real-world networks to test the proposed method, and the experimental results show the superiority of the proposed method over some state-of-the-art algorithms in terms of both the quality and the temporal smoothness of the detected community structures. The proposed method provides us with a stable and promising solution for the problem of community detection in dynamic networks.

Community Detection in Dynamic Networks: Equivalence Between Stochastic Blockmodels and Evolutionary Spectral Clustering

Community detection aims to identify densely connected groups of nodes in complex networks. Although a variety of methods have been proposed for community detection, the relationship between them is not well understood. Recently, researchers have shown the equivalence between modularity optimization and likelihood maximization in stochastic block models (SBMs) for static networks. Showing this equivalence is important for both understanding the different community detection methods and selecting the hyperparameters in the different algorithms in a more principled way. In this paper, we extend this equivalence for dynamic community detection algorithms. In particular, we show the equivalence of evolutionary spectral clustering to a variant of dynamic stochastic blockmodel. For this purpose, we first introduce a novel dynamic SBM where the evolution of communities over time is modeled with pairwise Markov random fields. We then show that the log-posterior of the proposed model is equivalent to the quality function of evolutionary spectral clustering. This equivalence is used to determine the forgetting factor in evolutionary spectral clustering and to develop two new algorithms for dynamic community detection. Compared to original evolutionary spectral clustering, the forgetting factor is time-dependent and derived directly from the parameters of the proposed dynamic SBM. The proposed algorithms are shown to be superior to state-of-the-art dynamic community detection methods for both simulated and real-world dynamic networks.

Co-regularized nonnegative matrix factorization for evolving community detection in dynamic networks

Community detection in static networks solely emphasizes the clustering accuracy, while evolving community detection in dynamic networks simultaneously takes into account both the clustering accuracy and clustering drift. The available evolutionary clustering algorithms are criticized for failing to fully characterize dynamics of networks and to accurately balance the clustering accuracy and clustering drift. To solve these problems, we propose a co-regularized evolutionary nonnegative matrix factorization for evolving communities in dynamic networks (Cr-ENMF). Specifically, both the network and communities at the previous time step are utilized to characterize the clustering drift, which are incorporated into the objective function of Cr-ENMF by regularization. We show that the well-known temporal smoothness framework for evolutionary clustering is a special case of the proposed framework, and prove the equivalence between Cr-ENMF and evolutionary clustering. Thereafter, an iterative strategy is presented to optimize the objective function. The experimental results over both artificial and real world dynamic networks illustrate that Cr-ENMF outperforms state-of-the-art approaches.

Community detection in dynamic networks using constraint non-negative matrix factorization

Community detection in dynamic networks using constraint non-negative matrix factorization

An Evolutionary Approach Based on Symmetric Nonnegative Matrix Factorization for Community Detection in Dynamic Networks

Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.

A Consensus Community-Based Particle Swarm Optimization for Dynamic Community Detection.

The community detection in dynamic networks is essential for important applications such as social network analysis. Such detection requires simultaneous maximization of the clustering accuracy at the current time step while minimization of the clustering drift between two successive time steps. In most situations, such objectives are often in conflict with each other. This article proposes the concept of consensus community. Knowledge from the previous step is obtained by extracting the intrapopulation consensus communities from the optimal population of the previous step. Subsequently, the intrapopulation consensus communities of the previous step obtained is voted by the population of the current time step during the evolutionary process. A subset of the consensus communities, which receives a high support rate, will be recognized as the interpopulation consensus communities of the previous and current steps. Interpopulation consensus communities are the knowledge that can be transferred from the previous to the current step. The population of the current time step can evolve toward the direction similar to the population in the previous time step by retaining such interpopulation consensus community during the evolutionary process. Community structure is subjected to evaluation, update, and mutation events, which are directed by using interpopulation consensus community information during the evolutionary process. The experimental results over many artificial and real-world dynamic networks illustrate that the proposed method produces more accurate and robust results than those of the state-of-the-art approaches.

Community detection in dynamic signed network: an intimacy evolutionary clustering algorithm

The tremendous development of community detection in dynamic networks have been witnessed in recent years. In this paper, intimacy evolutionary clustering algorithm is proposed to detect community structure in dynamic networks. Firstly, the time weighted similarity matrix is utilized and calculated to grasp time variation during the community evolution. Secondly, the differential equations are adopted to learn the intimacy evolutionary behaviors. During the interactions, intimacy between two nodes would be updated based on the iteration model. Nodes with higher intimacy would gather into the same cluster and nodes with lower intimacy would get away, then the community structure would be formed in dynamic networks. The extensive experiments are conducted on both real-world and synthetic signed networks to show the efficiency of detection performance. Moreover, the presented method achieves better detection performance compared with several better algorithms in terms of detection accuracy.

Disentangling group and link persistence in dynamic stochastic block models

We study the inference of a model of dynamic networks in which both communities and links keep memory of previous network states. By considering maximum likelihood inference from single snapshot observations of the network, we show that link persistence makes the inference of communities harder, decreasing the detectability threshold, while community persistence tends to make it easier. We analytically show that communities inferred from single network snapshot can share a maximum overlap with the underlying communities of a specific previous instant in time. This leads to time-lagged inference: the identification of past communities rather than present ones. Finally we compute the time lag and propose a corrected algorithm, the lagged snapshot dynamic algorithm, for community detection in dynamic networks. We analytically and numerically characterize the detectability transitions of such algorithm as a function of the memory parameters of the model and we make a comparison with a full dynamic inference.

Multiobjective discrete particle swarm optimization for community detection in dynamic networks

Tracking and identifying the dynamic patterns of evolving communities has recently drawn great attention. How to detect the community structure in a dynamic network has become a popular problem in the field of complex network and evolutionary computing. As a new concept, evolutionary clustering, is proposed to detect the process of dynamic networks under the temporal smoothness framework. Evolutionary-based clustering approaches try to maximize clustering accuracy at the current time step and minimize clustering drift at two successive time steps. But the low accuracy and the pre-setting of parameters limit their effectiveness. In order to overcome these weaknesses, in this paper, the community detection in a dynamic network is transformed into a multiobjective optimization problem. Specifically, we propose a novel decomposition strategy for multiobjective discrete particle swarm optimizationm, which balances the accuracy and the smoothness. The experimental results on synthetic and real-world datasets demonstrate the superiority of the proposed method compared with other state-of-the-art methods.

A multiobjective discrete bat algorithm for community detection in dynamic networks

Some evolutionary based clustering approaches for community detection in dynamic networks need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and improve the quality of detecting communities in dynamic network further, a multiobjective discrete bat algorithm (MDBA) is proposed to detect community structure in dynamic networks in this paper. In the proposed algorithm, the bat location updating strategy is designed in discrete form. In addition, turbulence operation and mutation strategy are presented to guarantee the diversity of the population. The non-dominated sorting and crowding distance mechanism are used to keep good solutions during the generation. The experimental results both on synthetic and real networks show that MDBA algorithm is competitive and will get higher accuracy and lower error rate than the compared algorithms.

A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks

Community detection in temporal social networks is an increasingly challenging subject in network analysis. The Label Propagation Algorithm (LPA) is a simple and fast approach for community detection in dynamic networks. However, it tends to generate monster communities which decrease the accuracy of community detection, especially in dynamic social networks. In this paper, we propose a modified LPA, called Spreading Activation Label Propagation Algorithm in order to solve the problem. This method assigns a property, called activation value, to each label, where pairs (label name, activation value) are propagated by spreading activation process and the LPA. Furthermore, this algorithm uses two weighting algorithms, where each of them corresponds to one variation of the proposed method. Here, the variations of the proposed method and other available methods on real and synthetic networks are implemented. Experimental results on both real and synthetic networks show that all variations of the proposed method detect communities more accurately compared to the benchmark methods while they are slower than these methods.

Community detection in dynamic networks with spark

Detecting the evolution of communities within dynamically changing networks is important to understand the latent structure of complex large graphs. In this paper, we present an algorithm to detect real-time communities in dynamically changing networks. We demonstrate the proposed methodology through a case study in peer-to-peer (P2P) botnet detection which is one of the major threats to network security for serving as the infrastructure that is responsible for various cyber crimes. Our method considers online community structure from time to time and improves efficiency by maintaining the same level of accuracy of community detection over time. Experimental evaluation on Apache Spark implementation of the method showed that the execution time improves over dynamic version of Girvan-Newman community detection algorithm while having a higher accuracy level.