Existing Community Detection Methods Research Articles

Game Theoretic Clustering for Finding Strong Communities.

We address the challenge of identifying meaningful communities by proposing a model based on convex game theory and a measure of community strength. Many existing community detection methods fail to provide unique solutions, and it remains unclear how the solutions depend on initial conditions. Our approach identifies strong communities with a hierarchical structure, visualizable as a dendrogram, and computable in polynomial time using submodular function minimization. This framework extends beyond graphs to hypergraphs or even polymatroids. In the case when the model is graphical, a more efficient algorithm based on the max-flow min-cut algorithm can be devised. Though not achieving near-linear time complexity, the pursuit of practical algorithms is an intriguing avenue for future research. Our work serves as the foundation, offering an analytical framework that yields unique solutions with clear operational meaning for the communities identified.

METHODS: A meta-path-based method for heterogeneous community detection in the open source software ecosystem

Detecting communities in the open source software (OSS) ecosystem can help understand the collaborations in the open source software ecosystem and promote an understanding of the dynamics of the ecosystem. However, most existing community detection methods are designed for homogeneous networks, whereas the OSS ecosystem is a heterogeneous network. Therefore, we propose a meta-path-based method for heterogeneous community detection in the OSS ecosystem (METHODS). METHODS comprises four steps. Firstly, a heterogeneous information network is constructed based on meta-paths. Secondly, the Canopy algorithm is used to obtain the number of initial communities. Thirdly, the skip-gram model is used to identify seed nodes for community detection. Finally, METHODS detects heterogeneous communities around the seed nodes. By defining a series of evaluation metrics and verifying these on GitHub datasets, METHODS achieves the best performance of all the other methods. Moreover, the case studies on GitHub also shows METHODS can discover latent communities whose members are potentially relevant.

Identification of Communities With Multi-Semantics via Bayesian Generative Model

Discovering communities is an essential step in the analysis of complex systems, and it has two purposes: to identify functional modules and to interpret semantics. However, most of the existing community detection methods only focused on identify communities, while learning the semantics interpretation of communities has not been fully studied. In this paper, we focused on the problem of identifying communities and learning the semantics interpretation of modules jointly in an end-to-end model. We designed a novel generative model which combines two closely related parts, one for community discovery and the other for content clustering and semantics interpretation. By extracting the potential correlation between these two parts, our new method is not only robust to discovering communities, but also able to provide a community with more than one semantic topic. As for model inference, we developed a variational algorithm from a Bayesian point of view. Experimental results on the artificial benchmark and real networks showed the superior performance of the proposed approach over existing methods in terms of effectiveness and efficiency. We also analyzed semantic interpretability of community detection results through a case study over a large-scale music platform dataset.

CP-GNN: A Software for Community Detection in Heterogeneous Information Networks

<h2>Abstract</h2> Recently, the topic of community detection (CD) in heterogeneous information networks (HINs), which contain multiple types of nodes and edges, has received much attention. However, existing CD methods could not well exploit the high-order relationship among the nodes to detect communities. To alleviate this issue, we propose to use the concept of context path to model the high-order relationship among nodes, and develop a novel context path-based graph neural network (GNN) software, called CP-GNN. It can not only learn the embeddings of nodes for detecting communities accurately, but also well capture the high-order relations between nodes unsupervisedly, which enables great convenience to the study of communities and real-world applications.

Hybrid-order Stochastic Block Model

Community detection is a research hotspot in machine learning and data mining. However, most of the existing community detection methods only rely on the lower-order connectivity patterns, while ignoring the higher-order connectivity patterns, and unable to capture the building blocks of the complex network. In recent years, some community detection methods based on higher-order structures have been developed, but they mainly focus on the motif network composed of higher-order structures, which violate the original lower-order topological structure and are affected by the fragmentation issue, resulting in the deviation of community detection results. Therefore, there is still a lack of community detection methods that can effectively utilize higher-order connectivity patterns and lower-order connectivity patterns. To overcome the above limitations, this paper proposes the Hybrid-order Stochastic Block Model (HSBM) from the perspective of the generative model. Based on the classical stochastic block model, the generation of lower-order structure and higher-order structure of the network is modeled uniformly, and the original topological properties of the network are maintained while using higher-order connectivity patterns. At the same time, a heuristic algorithm for community detection is proposed to optimize the objective function. Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches.

CDCN: A New NMF‐Based Community Detection Method with Community Structures and Node Attributes

Community discovery can discover the community structure in a network, and it provides consumers with personalized services and information pushing. It plays an important role in promoting the intelligence of the network society. Most community networks have a community structure whose vertices are gathered into groups which is significant for network data mining and identification. Existing community detection methods explore the original network topology, but they do not make the full use of the inherent semantic information on nodes, e.g., node attributes. To solve the problem, we explore networks by considering both the original network topology and inherent community structures. In this paper, we propose a novel nonnegative matrix factorization (NMF) model that is divided into two parts, the community structure matrix and the node attribute matrix, and we present a matrix updating method to deal with the nonnegative matrix factorization optimization problem. NMF can achieve large‐scale multidimensional data reduction processing to discover the internal relationships between networks and find the degree of network association. The community structure matrix that we proposed provides more information about the network structure by considering the relationships between nodes that connect directly or share similar neighboring nodes. The use of node attributes provides a semantic interpretation for the community structure. We conduct experiments on attributed graph datasets with overlapping and nonoverlapping communities. The results of the experiments show that the performances of the F1‐Score and Jaccard‐Similarity in the overlapping community and the performances of normalized mutual information (NMI) and accuracy (AC) in the nonoverlapping community are significantly improved. Our proposed model achieves significant improvements in terms of its accuracy and relevance compared with the state‐of‐the‐art approaches.

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

LPX: Overlapping community detection based on X‐means and label propagation algorithm in attributed networks

AbstractTraditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.

Multiple-kernel combination fuzzy clustering for community detection

Community detection on social network is a challenging task, and the multiple-kernel learning method is gaining popularity. In this paper, we propose a new multiple-kernel combination algorithm for community partitioning. We study several base kernel matrices from the adjacency matrix of a network. By adjusting the weights of different base kernel matrices, a new kernel matrix is constructed using linear combination of those matrices. To partition networks whose number of communities are known in advance, we derive a new kernel matrix which forms a basis for community partitioning. We further propose a novel robust multiple-kernel combination-based fuzzy clustering algorithm. Extensive experiments are conducted on many real-world networks that contain ground truth on community structures. The experimental results indicate that the proposed algorithm is more efficient than other existing community detection methods and related kernel clustering algorithms. This study demonstrates the feasibility and efficiency of the multiple-kernel learning method for community detection.

Overlapping community detection with a novel hybrid metaheuristic optimisation algorithm

Social networks are ubiquitous in our daily life. Due to the rapid development of information and electronic technology, social networks are becoming more and more complex in terms of sizes and contents. It is of paramount significance to analyse the structures of social networks in order to unveil the myth beneath complex social networks. Network community detection is recognised as a fundamental tool towards social networks analytics. As a consequence, numerical community detection methods are proposed in the literature. For a real-world social network, an individual may possess multiple memberships, while the existing community detection methods are mainly designed for non-overlapping situations. With regard to this, this paper proposes a hybrid metaheuristic method to detect overlapping communities in social networks. In the proposed method, the overlapping community detection problem is formulated as an optimisation problem and a novel bat optimisation algorithm is designed to solve the established optimisation model. To enhance the searchability of the proposed algorithm, a local search operator based on tabu search is introduced. To validate the effectiveness of the proposed algorithm, experiments on benchmark and real-world social networks are carried out. The experiments indicate that the proposed algorithm is promising for overlapping community detection.

An optimisation tool for robust community detection algorithms using content and topology information

With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information.

Overlapping Community Detection in Directed and Undirected Attributed Networks Using a Multiobjective Evolutionary Algorithm

In many real-world networks, the structural connections of networks and the attributes about each node are always available. We typically call such graphs attributed networks, in which attributes always play the same important role in community detection as the topological structure. It is shown that the very existence of overlapping communities is one of the most important characteristics of various complex networks, while the majority of the existing community detection methods was designed for detecting separated communities in attributed networks. Therefore, it is quite challenging to detect meaningful overlapping structures with the combination of node attributes and topological structures. Therefore, in this article, we propose a multiobjective evolutionary algorithm based on the similarity attribute for overlapping community detection in attributed networks (MOEA-SA OV ). In MOEA-SA OV , a modified extended modularity EQOV , dealing with both directed and undirected networks, is well designed as the first objective. Another objective employed is the attribute similarity SA . Then, a novel encoding and decoding strategy is designed to realize the goal of representing overlapping communities efficiently. MOEA-SA OV runs under the framework of the nondominated sorting genetic algorithm II (NSGA-II) and can automatically determine the number of communities. In the experiments, the performance of MOEA-SA OV is validated on both synthetic and real-world networks, and the experimental results demonstrate that our method can effectively find Pareto fronts about overlapping community structures with practical significance in both directed and undirected attributed networks.

SCE: Subspace-based core expansion method for community detection in complex networks

Community detection is a way to understand the mesoscale characteristics of networked systems and has received much attention recently. Most existing community detection methods suffer from several problems including; weak stability due to employing a randomness factor, requiring the number of communities before starting the community identification process, and unable to recognize communities of various sizes. To overcome these challenges, in this paper a novel subspace-based core expansion method is proposed for identifying non-overlapping communities. The proposed method consists of three main steps. In the first step, the graph is mapped to a low dimensional space using a linear sparse coding method. The main idea behind the mapping strategy is that each data point within a combination of subspaces can be represented as a linear combination of other points. In the second step, a novel node ranking strategy is developed to calculate the goodness of nodes to be considered in identifying community cores. Finally, a novel label propagation mechanism is proposed to form final communities. Several experiments are performed to evaluate the effectiveness of the proposed method on real and synthetic networks. Obtained results reveal the better performance of the proposed method compared to some baseline and state-of-the-art community detection methods.

User interest community detection on social media using collaborative filtering

Community detection in microblogging environment has become an important tool to understand the emerging events. Most existing community detection methods only use network topology of users to identify optimal communities. These methods ignore the structural information of the posts and the semantic information of users’ interests. To overcome these challenges, this paper uses User Interest Community Detection model to analyze text streams from microblogging sites for detecting users’ interest communities. We propose HITS Latent Dirichlet Allocation model based on modified Hypertext Induced Topic Search and Latent Dirichlet Allocation to distil emerging interests and high-influence users by reducing negative impact of non-related users and its interests. Moreover, we propose HITS Label Propagation Algorithm method based on Label Propagation Algorithm and Collaborative Filtering to segregate the community interests of users more accurately and efficiently. Our experimental results demonstrate the effectiveness of our model on users’ interest community detection and in addressing the data sparsity problem of the posts.

NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach.

With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.

From the User to the Medium: Neural Profiling Across Web Communities

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.

Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.

Transient Community Detection and Its Application to Data Forwarding in Delay Tolerant Networks

Community detection has received considerable attention because of its applications to many practical problems in mobile networks. However, when considering temporal information associated with a community (i.e., transient community), most existing community detection methods fail due to their aggregation of contact information into a single weighted or unweighted network. In this paper, we propose a contact-burst-based clustering method to detect transient communities by exploiting pairwise contact processes. In this method, we formulate each pairwise contact process as a regular appearance of contact bursts, during which most contacts between the pair of nodes happen. Based on this formulation, we detect transient communities by clustering the pairs of nodes with similar contact bursts. Since it is difficult to collect global contact information at individual nodes, we further propose a distributed method to detect transient communities. In addition to transient community detection, we also propose a new data forwarding strategy for delay tolerant networks, in which transient communities serve as the data forwarding unit. Evaluation results show that our strategy can achieve a much higher data delivery ratio than traditional community-based strategies with comparable network overhead.

Community Detection Based on Differential Evolution Using Social Spider Optimization

Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.