Overlapping Community Discovery Research Articles

Two-stage multi-objective evolutionary algorithm for overlapping community discovery.

As one of the essential topological structures in complex networks, community structure has significant theoretical and application value and has attracted the attention of researchers in many fields. In a social network, individuals may belong to different communities simultaneously, such as a workgroup and a hobby group. Therefore, overlapping community discovery can help us understand and model the network structure of these multiple relationships more accurately. This article proposes a two-stage multi-objective evolutionary algorithm for overlapping community discovery problem. First, using the initialization method to divide the central node based on node degree, combined with the cross-mutation evolution strategy of the genome matrix, the first stage of non-overlapping community division is completed on the decomposition-based multi-objective optimization framework. Then, based on the result set of the first stage, appropriate nodes are selected from each individual's community as the central node of the initial population in the second stage, and the fuzzy threshold is optimized through the fuzzy clustering method based on evolutionary calculation and the feedback model, to find reasonable overlapping nodes. Finally, tests are conducted on synthetic datasets and real datasets. The statistical results demonstrate that compared with other representative algorithms, this algorithm performs optimally on test instances and has better results.

RETRACTED: NDI-PPO: Node degree increment-based proximal policy optimization method for employment community discovery in online social networks

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Multi-Purpose function-based Parliamentary Optimization Framework for Community Detection over social media

This paper presents a multi-purpose function-based Parliamentary Optimization, community detection methods. Initially, the population of Parliamentary Optimization Algorithm was created in a Python environment for the data used. The population formed was divided into a certain number of groups, and the power values of each group were calculated. While strong groups show joining according to the determined combination probability value, the vulnerable groups are eliminated from the population according to the determined deletion probability. The result for the problem has been approached. The program steps continued until all groups were combined and the termination condition of the algorithm was met; the individual with the highest eligibility values among the remaining data in the last stage was accepted as the solution to the overlapping community discovery problem of the proposed algorithm. Subsequently, performs a proposed work evaluated over one artificial and four real network-based social media data sets. The comparison of feature evaluation is carried out to identify the influence of Single and Multi-Purpose functions on community detection performance. Finally, this paper gives a comparative analysis of proposed MPPOA algorithm worth three heuristic overlap community detection algorithm over six real social media data set.

Set pair three-way overlapping community discovery algorithm for weighted social internet of things

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.

The overlapping community discovery algorithm based on the local interaction model

In social networks, the traditional locally optimized overlapping community detection algorithm has a free-rider problem in community extension, which mainly relies on the structure information of nodes but ignores the node attributes. Therefore, in this paper, we redefine community based on theoretical analysis and propose an overlapping community discovery algorithm based on the local interaction model. By fusing node attributes and structural information, we first proposed an improved density peak fast search method to obtain multiple core nodes in the community. Then, according to the interaction range and interaction mode of the core node, we established a local interaction model of the core node, which converts the interaction strength or the number of common attributes between nodes in the network into the change of the distance between nodes. Finally, according to the proposed improved clustering algorithm, we obtain the community where the core node is located and merge the communities with a high degree of overlap. The experimental results show that compared with other similar community discovery algorithms, the proposed method outperforms the state-of-the-art approaches for community detections.

Edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction in social media data streams

AbstractEdge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber‐physical‐social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper‐connected smart sensing devices. As a part of future CPSS, online social networks are large‐scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state‐of‐the‐art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset.

Application of Overlapping Community Discovery Algorithm in Complex Network Big Data

Application of Overlapping Community Discovery Algorithm in Complex Network Big Data

Overlapping Community Discovery Method Based on Two Expansions of Seeds

The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms.

Overlapping Community Discovery for Identifying Key Research Themes

Identifying key research themes is an effective way to chart knowledge structures in a field of research and, in turn, stimulate new ideas and innovation. Most thematic analyses of a research field are based on some form of network analysis, e.g., citations and cowords, and most of these networks are made up of cohesive, highly overlapping groups of nodes. Based on the suggestion that the “universal features” of networks are to be found in these overlapping communities, we argue that these same communities in a keyword network should reveal the key research themes in a field of study. With no traditional method with which to test our theory, we combined a cluster percolation algorithm with a Word2Vec model, and in a case study on information science, we were not only able to detect the overlapping communities in a keyword similarity network, but we also found a new perspective on the importance of overlapping communities as a way to identify a field's key research themes.

Comparison of Multiple Different Overlapping Community Discovery Algorithms

When analysing a real-world problem, it can be transformed into a complex network, and then its structure is divided to find out the overlapping part and study the hidden laws. In this paper, three overlapping community recognition algorithms are briefly introduced, i.e., overlapping community partitioning based on label propagation (OCPLP), footpad skin optical clearing agent (FSOCA) and the genetic algorithm (GA), and then the three artificial networks generated by the LFR tool are simulated and analysed through matrix laboratory (MATLAB) software. The results show that the increase of network complexity will reduce the recognition performance of the three algorithms. In the three algorithms, GA is relatively less affected, which always has the highest recognition performance. OCPLP is the most affected, which has the worst performance. In terms of the speed of recognition, GA is also the fastest, and OCPLP is the slowest. In summary, GA is more suitable for the search of overlapping communities in the network than that of OCPLP and FSOCA.

Comparison of multiple different overlapping community discovery algorithms

Comparison of multiple different overlapping community discovery algorithms

Integrating overlapping community discovery and role analysis: Bayesian probabilistic generative modeling and mean-field variational inference

The joint modeling of community discovery and role analysis was shown useful to explain, predict and reason on network topology. Nonetheless, earlier research on the integration of both tasks suffers from major limitations. Foremost, a key aspect of role analysis, i.e., the strength of role-to-role interactions, is ignored. Moreover, two fundamental properties of networks are disregarded, i.e., heterogeneity in the connectivity structure of communities and the growing link probability with node involvement in common communities. Additionally, scalability with network size is limited.In this manuscript, we incrementally develop two new machine learning approaches to deal with the foresaid issues. The proposed approaches consist in performing inference under as many Bayesian generative models of networks with overlapping communities and roles. Under both models, nodes are associated with communities and roles through suitable affiliations, that are dichotomized for link directionality. The strength of such affiliations is captured through nonnegative latent random variables, drawn from Gamma priors. Besides, link establishment is explained by both models through Poisson distributions. In particular, under the second model, the parameterizing rate of the Poisson distribution also accommodates the strength of role-to-role interactions, as captured via latent mixed-membership stochastic blockmodeling. On sparse networks, the adoption of the Poisson distribution expedites model inference. On this point, mean-field variational inference is derived and implemented as a coordinate-ascent algorithm, for the exploratory and unsupervised analysis of node affiliations. Comparative experiments on several real-world networks demonstrate the superiority of the proposed approaches in community discovery, link prediction as well as scalability.

Overlapping Community Discovery Based on the Combination of Node Influence and β-Connected Neighbors

We propose an overlapping community discovery algorithm that combines node influence and [Formula: see text]-connected neighbors for effectively detecting the overlapping community structure of complex networks. On the basis of the node influence and [Formula: see text]-connected neighbors, our method accurately detects the core node community and uses the improved similarity between the node and community to expand the core node community. Accordingly, the discovery and optimization of network overlapping communities are realized. Experiments on artificial and real-world networks demonstrate that our method significantly and consistently outperforms other comparison methods.

Optimal community detection method based on average mutual information

This study proposes an optimal community detection method based on average mutual information (AMI). We calculate the optimal community partition using AMI. This method is applied to the non-overlapping community detection algorithm (GN) and the overlapping community detection algorithm (COPRA), and the AMI-GN algorithm and the AMI-COPRA algorithm are generated respectively. Compared with the performance of the original GN, FN, and IE algorithm, experimental results show that AMI-GN algorithm improves the quality of community detection. Furthermore, compared with the performance of the original COPRA algorithm and the LPPB algorithm, experimental results show that the AMI-COPRA algorithm improves the stability of the original COPRA algorithm, reduces the average number of iterations, and accelerates the convergence speed of the algorithm. Moreover, compared with the LPPB algorithm, the AMI-COPRA algorithm reveals that the quality of the community shows a little difference, but is more stable than the LPPB algorithm. Our study shows that the AMI-based methods can improve the performance of typical non-overlapping community discovery algorithms and overlapping community discovery algorithms.

Overlapping Community Detection Algorithm Based on Coarsening and Local Overlapping Modularity

Community detection is an important research direction in the field of complex network analysis. It aims to discover community structures in complex networks. Algorithms based on dynamic distance mechanism can find stable communities with various shapes. However, they still cannot discover overlapping or outlier communities. This paper proposes an overlapping community discovery algorithm based on coarsening and local overlapping modularity. First, to reduce the running time, a new equation for computing the local overlapping modularity increment is derived. This equation finds the overlapping communities, accurately and quickly. Second, a new similarity measuring strategy is designed to reduce the number of outlier communities. The experiments on artificial and real datasets show that the proposed algorithm can discover the overlapping communities, accurately and efficiently.

An Entity-Association-Based Matrix Factorization Recommendation Algorithm

Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user's social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users' interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first detect the community structure using overlapping community discovery algorithm, and mine the clustering information of user interest preference by a fuzzy clustering algorithm based on the project category information. On the other hand, we use project-category attribution matrix and user-project score matrix to get project comprehensive similarity and compute project feature matrix based on Entity Relation Decomposition. Fusing the user clustering information and project information together, we get Entity-Association-based Matrix Factorization(EAMF) model which can be used to predict user ratings. The proposed algorithm is compared with other algorithms on the Yelp dataset. Experimental studies show that the proposed algorithm leads to a substantial increase in recommendation accuracy on Yelp data set.

Adaptive community detection incorporating topology and content in social networks✰

In social network analysis, community detection is a basic step to understand the structure and function of networks. Some conventional community detection methods may have limited performance because they merely focus on the networks’ topological structure. Besides topology, content information is another significant aspect of social networks. Although some state-of-the-art methods started to combine these two aspects of information for the sake of the improvement of community partitioning, they often assume that topology and content carry similar information. In fact, for some examples of social networks, the hidden characteristics of content may unexpectedly mismatch with topology. To better cope with such situations, we introduce a novel community detection method under the framework of non-negative matrix factorization (NMF). Our proposed method integrates topology as well as content of networks and has an adaptive parameter (with two variations) to effectively control the contribution of content with respect to the identified mismatch degree. Based on the disjoint community partition result, we also introduce an additional overlapping community discovery algorithm, so that our new method can meet the application requirements of both disjoint and overlapping community detection. The case study using real social networks shows that our new method can simultaneously obtain the community structures and their corresponding semantic description, which is helpful to understand the semantics of communities. Related performance evaluations on both artificial and real networks further indicate that our method outperforms some state-of-the-art methods while exhibiting more robust behavior when the mismatch between topology and content is observed.

Dynamic Trustworthiness Overlapping Community Discovery in Mobile Internet of Things

In the mobile Internet of Things (IoT), dynamic community discovery has attracted increasing attention, because it can provide strong reliability and security for mobile IoT applications, such as data forwarding or fusion. However, when building community structures, most traditional global detection methods only focus on the encounter ratio between nodes and need to have holistic cognition for the whole network structure information. Thus, in IoT applications with large-scale or incomplete structures, there are deficiencies, such as lower security and higher time complexity. This paper proposes a detection scheme for dynamic trustworthiness overlapping community, called D2-TOC. This scheme first employs evidence-based data between node pairs, such as contact probability , recency , and service degree , to construct the trustworthiness relationships between devices and the network model of mobile IoT, which can provide the security guarantee for data interaction from the start. Second, the D2-TOC uses optimized random walks and seeds expansion methods to fulfill the initialization and evolution of overlapping communities, which avoids the high temporal and spatial complexity generated by global community detection. Finally, the experimental results have proven that, in mobile IoT scenarios, our D2-TOC algorithm has a better performance than the DEMON, Kim–Han, LFM, and UEOC in accuracy and efficiency.

Overlapping Community Discovery Algorithm Based on Hierarchical Agglomerative Clustering

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable.

Latent Network Features and Overlapping Community Discovery via Boolean Intersection Representations.

We propose a new latent Boolean feature model for complex networks that captures different types of node interactions and network communities. The model is based on a new concept in graph theory, termed the Boolean intersection representation of a graph, which generalizes the notion of an intersection representation. We mostly focus on one form of Boolean intersection, termed cointersection, and describe how to use this representation to deduce node feature sets and their communities. We derive several general bounds on the minimum number of features used in cointersection representations and discuss graph families for which exact cointersection characterizations are possible. Our results also include algorithms for finding optimal and approximate cointersection representations of a graph.