Dynamic Community Detection Algorithm Research Articles

Dynamic community detection based on graph convolutional networks and contrastive learning

With the continuous development of technology and networks, real-life interactions are gradually being abstracted into social networks for study. Social circles are a fundamental structural feature that is prevalent on social networks. Thus, exploring social circle structure plays an important role in revealing the characteristics of complex social networks and provides guidance for understanding social behavior in real life. For example, it can aid in precision marketing, personalized recommendation, and knowledge dissemination within social circles. One of the important means of identifying social network circles lies on community detection algorithms. However, real-world social networks are often dynamic and can be studied and analyzed by building dynamic networks, while existing dynamic network community detection methods tend to ignore the global structure information and time-series information of nodes. To address this problem, this paper proposes a dynamic network community detection algorithm based on graph convolutional neural networks and contrastive learning, which fully captures the adjacent characteristics between nodes based on the correlation information and leverages the feature smoothing strategy to efficiently extract node representations of dynamic networks under unsupervised scenario. Specifically, the proposed algorithm first utilizes node correlation based aggregation strategy to compute the feature matrix for single time-step of the dynamic network. Then, mutual information maximization is implemented based on cross-entropy between learned local and global representations. To reduce the computational overhead in the optimization process, an additional LSTM module is further equipped for updating the parameters of graph convolutional networks in each time-step. Additionally, a contrastive learning based network smoothing strategy is designed to minimize the feature differences between neighboring nodes. Comparative experiments demonstrate that the proposed algorithm achieves excellent performance on both synthetic and real networks.

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.

Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm.

The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a group of community-divided solutions. The evolution of the population uses the crossover and mutation operations of the genome matrix. To make the population in the evolution process closer to a better community division result, we develop a new strategy for node occupancy assignment and cooperate with mutation operators, aiming at the boundary nodes in the connection between the community and the connection between communities, by calculating the comparison node. The occupancy rate of the community with the neighbor node, the node is assigned to the community with the highest occupancy rate, and the authenticity of the community division is improved. In addition, to select high-quality final solutions from candidate solutions, we use a rationalized selection strategy from the external population size to obtain better time costs through smaller snapshot quality loss. Finally, comparative experiments with other representative dynamic community detection algorithms on synthetic and real datasets show that our proposed method has a better balance between snapshot quality and time cost.

Dynamic community detection including node attributes

Community detection is an important task in social network analysis. It is generally based on the links of a static network, where groups of connected nodes can be found. Real-world problems, however, are often characterized by behavior that changes over time. In such cases, we need dynamic community detection algorithms because they better capture the underlying dynamics. Furthermore, the inclusion of node attributes provides a more robust approach since dynamic attribute values could also indicate changes in the communities. We propose an algorithm for COmmunity DEtection in Dynamic Attributed NETworks (CoDeDANet), which allows us to find groups in dynamic attributed networks using both the link and node information. In the first phase, based on spectral clustering, the attributes’ importance is optimized in a setting that joins the nodes’ features with a topological structure. In a second phase, tensors are used to consider not only current but also past information. The algorithm was tested on four synthetic networks and two real-world social networks. The results show that CoDeDANet outperforms the other state-of-the-art community detection algorithms.

Core node knowledge based multi-objective particle swarm optimization for dynamic community detection

Community detection aims to classify nodes with similar attributes, and it becomes more difficult when the network structure changes over time. In order to tap into the evolution of dynamic networks, nodes with high criticality in dynamic networks clearly need to be attended. In this paper, a novel dynamic community detection algorithm based on particle swarm optimization is proposed. The core nodes in the network are obtained by calculating the resistance distance of each node. The nodes associated with these core nodes that make up the constant community. The constant community should be retained is the knowledge gained from the evolution of the core nodes in two consecutive time steps. To ensure the quality of community division at the current time step and the smoothness of two consecutive time steps, a new particle updating parameter is proposed, which is based on the optimal individual at the previous time step and the population at the current time step. Compared with the other well-known related algorithms, experimental result on many real-world and synthetic networks have illustrated the higher accuracy and stability of the proposed.

Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data

The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities.

Dynamic community detection over evolving networks based on the optimized deep graph infomax.

As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-based methods stand out by embedding the global network structure and properties into a low-dimensional representation for community detection. However, such kinds of methods can only be utilized at each single time step independently. As a consequence, the information of all time steps requires to be stored, which increases the computational cost. Besides this, the neighbors of target nodes are considered equally when aggregating nodes in networks, which omits the local structural feature of networks and influences the accuracy of node representation. To overcome such shortcomings, this paper proposes a novel optimized dynamic deep graph infomax (ODDGI) method for dynamic community detection. Since the recurrent neural network (RNN) can capture the dynamism of networks while avoiding storing all information of dynamic networks, our ODDGI utilizes RNN to update deep graph infomax parameters, and thus, there is no need to store the knowledge of nodes in full time span anymore. Moreover, the importance of nodes is considered using similarity aggregation strategy to improve the accuracy of node representation. The experimental results on both the real-world and synthetic networks prove that our method surpasses other state-of-the-art dynamic community detection algorithms in clustering accuracy and stability.

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.

Dynamic community discovery via common subspace projection

Detecting communities of highly internal and low external interactions in dynamically evolving networks has become increasingly important owing to its wide applications in divers fields. Conventional solutions based on static community detection approaches treat each snapshot of dynamic networks independently, which may fragment communities in time (Aynaud T and Guillaume J L 2010 8th Int. Symp. on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (IEEE) pp 513–9), resulting in the problem of instability. In this work, we develop a novel dynamic community detection algorithm by leveraging the encoding–decoding scheme present in a succinct network representation method to reconstruct each snapshot via a common low-dimensional subspace, which can remove non-significant links and highlight the community structures, resulting in the mitigation of community instability to a large degree. We conduct experiments on simulated data and real social networking data with ground truths (GT) and compare the proposed method with several baselines. Our method is shown to be more stable without missing communities and more effective than the baselines with competitive performance. The distribution of community size in our method is more in line with the real distribution than those of the baselines at the same time.

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.

Identifying Communities in Dynamic Networks Using Information Dynamics.

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

C-Blondel: An Efficient Louvain-Based Dynamic Community Detection Algorithm

One of the most interesting topics in the scope of social network analysis is dynamic community detection, keeping track of communities’ evolutions in a dynamic network. This article introduces a new Louvain-based dynamic community detection algorithm relied on the derived knowledge of the previous steps of the network evolution. The algorithm builds a compressed graph, where its supernodes represent the detected communities of the previous step and its superedges show the edges among the supernodes. The algorithm not only constructs the compressed graph with low computational complexity but also detects the communities through the integration of the Louvain algorithm into the graph. The efficiency of the proposed algorithms is widely investigated in this article. By doing so, several evaluations have been performed over three standard real-world data sets, namely Enron Email, Cit-HepTh, and Facebook data sets. The obtained results indicate the superiority of the proposed algorithm with respect to the execution time as an efficiency metric. Likewise, the results show the modularity of the proposed algorithm as another effectiveness metric compared with the other well-known related algorithms.

A novel approach for choosing dynamic community detection algorithms using PROMETHEE II

In this paper, a decision support tool is developed in order to assist senior managers to choose a dynamic community detection algorithm. The main objective behind this novel approach is to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the study case. The suggested study ranks existing dynamic community detection algorithms in order to find the most appropriate one according to some preferences. For that purpose, we need a multi-criteria ranking method such as PROMETHEE II. This latter allows ranking alternatives from the best to the least according to some criteria extracted from the analysis of a provided questionnaire on dynamic community detection algorithms. The process of our approach is as follows: first, the questionnaire is provided to some experts in community detection domain. Second, answers are collected and analyzed in order to extract criteria. Third, a ranking of the algorithms with PROMETHEE II is operated according some extracted criteria and user preferences. Finally, the first ranked algorithm by PROMETHEE II is provided to the user in order to detect the communities.

DynaMo: Dynamic Community Detection by Incrementally Maximizing Modularity

Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, real-world networks, such as Facebook, LinkedIn and Twitter, are inherently growing rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks. We propose DynaMo, a novel modularity-based dynamic community detection algorithm, aiming to detect communities of dynamic networks as effective as repeatedly applying static algorithms but in a more efficient way. DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of dynamic networks. In the experimental evaluation, a comprehensive comparison has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real-world networks and 10,000 synthetic networks. Our results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by average) faster than Louvain algorithm.

RETRACTED ARTICLE: A dynamic clustering based method in community detection

Social networks are growing, community detection has become one of the hot topics in social network research. As various types of social networks continue to emerge, universal community detection approaches are becoming increasingly important. As the real community division is dynamic, the community structure will appear or disappear with the passage of time. Therefore, the authenticity and real-time of the division become the core foundation of community detection, and the design of a real-time algorithm based on real division has great challenges. In this paper, we propose a dynamic community detection algorithm dynamic clustering by fast search and find of density peaks (D-CFSFDP) based on the partition of nodes-follow relationships to improve the accuracy and adaptability of real complex community detection. In D-CFSFDP, a distance metric based on trust is defined, the user relationship in the social network is quantified as a distance matrix, and the size of the matrix element is used to measure the degree of the user relationship. Then we use kernel density estimation on the distance matrix, and compile the statistics of the impact of each node in the network. We combine the improved KD-Tree model and mean integrated squared error criterion to improve the calculation flow, so that it adapts to different sizes of data sets to improve the calculation accuracy. Based on the principle of density peak clustering and the community attributes, the internal structure and natural outside structure of the community can be obtained according to the distance between the nodes. Finally, the remaining nodes are allocated by distance to the corresponding community to complete the community division. The static community division is further extended to a dynamic detection algorithm that gets linear time complexity. Therefore, we can change the community structure by updating the node relationships in the network. Through the visualization software we can observed that, the D-CFSFDP algorithm give the results of community division with a clear natural and internal hierarchical structure. With the increase of community scale and difficulty of division, D-CFSFDP algorithm has excellent stability. In the real data set and the Douban network, the community division is more close to the real division result, the adaptability is good and the feasibility and validity are verified.

Dynamic Social Network Community Detection Algorithm Based on Hidden Markov Model

Dynamic Social Network Community Detection Algorithm Based on Hidden Markov Model

Parallel incremental dynamic community detection algorithm based on Spark

Parallel incremental dynamic community detection algorithm based on Spark

Toward a novel art inspired incremental community mining algorithm in dynamic social network

The problem of community detection has a long tradition in the area of data mining research and has many challenging aspects, particularly with regard to community detection in a time-varying context. Temporal smoothness is an important concept in this domain treated specifically by Evolutionary clustering. However, this approach is mostly designed for networks with low changes during different time steps. In this research, we present a new dynamic community detection algorithm in a representative-based category called ARTISON, which is inspired by the Adaptive Resonance Theory technique- a famous adaptive clustering model in neural networks. The proposed model is able to capture both low and high changes in the networks in binary. Further, our approach recognizes the number of communities automatically in both weighted and binary networks. Detailed experiments utilizing the MATLAB platform have yielded encouraging results for different measures and further motivate the application of such bio-inspired models in a dynamic social networking context.

The adaptive dynamic community detection algorithm based on the non-homogeneous random walking

With the changing of the habit and custom, people’s social activity tends to be changeable. It is required to have a community evolution analyzing method to mine the dynamic information in social network. For that, we design the random walking possibility function and the topology gain function to calculate the global influence matrix of the nodes. By the analysis of the global influence matrix, the clustering directions of the nodes can be obtained, thus the NRW (Non-Homogeneous Random Walk) method for detecting the static overlapping communities can be established. We design the ANRW (Adaptive Non-Homogeneous Random Walk) method via adapting the nodes impacted by the dynamic events based on the NRW. The ANRW combines the local community detection with dynamic adaptive adjustment to decrease the computational cost for ANRW. Furthermore, the ANRW treats the node as the calculating unity, thus the running manner of the ANRW is suitable to the parallel computing, which could meet the requirement of large dataset mining. Finally, by the experiment analysis, the efficiency of ANRW on dynamic community detection is verified.