community detection is a task of fundamental importance in social network analysis. community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on different metrics and domain of applications. Most of these methods are based on the existing of the non-Overlapping or sparse Overlapping communities. Moreover, the experimental analysis showed that, Overlapping areas of communities become denser than non-Overlapping area of communities. In this paper, significant methods of Overlapping community detection are compared according to well-known evaluation criteria. The experimental analyses on artificial network generation have shown that earlier methods of community detection will not discover Overlapping communities properly and we offered suggestions for resolving them.

A social network consists of some people who are related to each other through some similarities. The emergence and evolution of these networks and increasing rate of using them is the major cause for social network analysis to be a hot research topic. Using various algorithms, each network can be divided into some communities. So, each community includes some members of the social network. community detection is one of the most important and fundamental tasks in network analysis. It is a step towards understanding the patterns and characteristics of the complex systems they represent. In this paper, the state of the art algorithms for community detection are categorized into six categories (spectral clustering and centrality, quality function, Label propagation, Structure, Closeness, link clustering) based on their definition of the community and modelling the concept of Overlapping (existence of the nodes with membership in multiple communities). Next, these methods are implemented on various datasets and compared to each other. It is obvious from the results of performance measures, even in this small collection of data sets, no algorithm can be considered as the best community detection method for all kinds of networks.

Background and Objectives: Nowadays, social networks are recognized as significant sources of information exchange. Consequently, many organizations have chosen social networks as essential tools for marketing and brand management. Communities are essential structures that can enhance the performance of social networks by grouping nodes and analyzing the information derived from them. This subject becomes more important with the increase in information volume and the complexity of relationships in networks. The goal of community identification is to find subgraphs that are densely connected internally but loosely connected externally.Methods: While community detection has mostly been studied in static networks in the past, this paper focuses on dynamic networks and the influence of central nodes in forming communities. In the proposed algorithm, the network is captured through multiple snapshots. The initial snapshot calculates the influence of each node. Then, by selecting k nodes with higher influence, network communities are formed, and other nodes belong to the community with the most common edges. In the second step, after receiving the next snapshot, communities are updated. Then, k nodes with higher influence are selected, and their associated community is created if needed. If the previous community centers are not among the newly selected k nodes, the community is dissolved, and the nodes within it belong to other communities.Results: Based on the results obtained, the proposed algorithm has managed to achieve better results in most cases compared to the compared algorithms, especially in terms of modularity metrics. The reason behind this success could be attributed to the utilization of influential nodes in community formation.Conclusion: Drawing from the outcomes attained, the suggested algorithm has effectively outperformed the contrasted algorithms in a majority of instances, particularly concerning metrics related to modularity. This accomplishment can potentially be ascribed to the incorporation of influential nodes during the process of community formation.

In network analysis, the community is considered as a group of nodes that is densely connected with respect to the rest of the network. Detecting the community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There are various approaches in literature for community, Overlapping or disjoint, detection in networks. In recent years, many researchers have concentrated on feature learning and network embedding methods for nodes clustering. These methods map the network into a lower-dimensional representation space. In this paper, we propose a model for learning graph representation using deep neural networks. In this method, a nonlinear embedding of the original graph is fed to stacked auto-encoders for learning the model. Then an Overlapping clustering algorithm is employed to extract Overlapping communities. The effectiveness of the proposed model is investigated by conducting experiments on standard benchmarks and real-world datasets of varying sizes. Empirical results exhibit that the presented method outperforms some popular community detection methods.

detection of Overlapping communities in large complex social networks with intelligent agents, is an NP problem with great time complexity and large memory usage and no simultaneous online solution. Proposing a novel distributed label propagation approach can help to decrease the searching time and reduce the memory space usage. This paper presents a scalable distributed Overlapping community detection approach based on the label propagation method by proposing a novel algorithm and three new metrics to expand scalability and improve modularity through agent-based implementation and good memory allocation in a multi-core architecture. The experimental results of large real datasets over the state-of-the-art SLPA approach show that the execution time speeds up by 900% and the modularity improves by 3% to 100% thus producing fast and accurate detection of overlapped communities.