Non-overlapping Community Structure Research Articles

Adaptive opinion evolution process with opinion dynamics for large-scale group decision making: A novel approach based on overlapping community detection in social networks

The rapid development of Web2.0 technologies and social media platforms has developed a new paradigm that allows many individuals to participate in decision-making processes within online social networks, leading to the rise of social network group decision making (SNGDM). Existing research in SNGDM primarily focus on small-scale DMs, which may not be suitable for large-scale SNGDM problems due to the high costs and time constraints of adjustments. Moreover, the non-overlapping community structure in social network encounter several limitations, further complicating the resolution of large-scale SNGDM problems. In this study, we propose an adaptive opinion evolution process with opinion dynamics for large-scale SNGDM. The proposed approach comprises four stages: classification of decision makers (DMs), determination of community weights, consensus reaching process, and alternative selection. A real-world application is utilized to demonstrate the effectiveness of the proposed method, and a comparison with existing related works highlights the advantages and innovation of the proposed model.

Large-scale community detection based on core node and layer-by-layer label propagation

With the vigorous development of big data, the task of large-scale community structure detection is more challenging. In this paper, a large-scale community detection method based on core node and layer-by-layer label propagation is proposed, which is further extended to the detection of overlapping community structures. First, the core nodes whose node degree is greater than the average degree in the graph are found to make effective use of the feature that the core node is the potential community center. This will also avoid the impact of nodes with low node degrees on community structure detection. Then, starting from the core node, label propagation is carried out layer-by-layer according to the node degree and node connection, which effectively improves the accuracy of community detection. The node labels after label propagation are calibrated according to the current attraction of the community to the nodes, which effectively improves the situation of misclassification in the early community detection. Finally, overlapping community detection is carried out based on the non-overlapping community structure. At this time, the overlapping community detection result is more accurate and interpretable. The community detection results on 3 synthetic networks and 12 real datasets show that the proposed algorithm has more advantages than four non-overlapping community detection methods and two overlapping community detection methods.

Networks in the mind – what communities reveal about the structure of the lexicon

Abstract The mental lexicon stores words and information about words. The lexicon is seen by many researchers as a network, where lexical units are nodes and the different links between the units are connections. Based on the analysis of a word association network, in this article we show that different kinds of associative connections exist in the mental lexicon. Our analysis is based on a word association database from the agglutinative language Hungarian. We use communities – closely knit groups – of the lexicon to provide evidence for the existence and coexistence of different connections. We search for communities in the database using two different algorithms, enabling us to see the overlapping (a word belongs to multiple communities) and non-overlapping (a word belongs to only one community) community structures. Our results show that the network of the lexicon is organized by semantic, phonetic, syntactic and grammatical connections, but encyclopedic knowledge and individual experiences are also shaping the associative structure. We also show that words may be connected not just by one, but more types of connections at the same time.

Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density

Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

A new method for detecting communities in network based on the affinity between nodes

It has been found that many networks display community structure, analysis of the com-munity structure in the network is meaningful for understanding the real-world network. Recently, many community discovery methods have been proposed, most of which are based on the assumption that nodes in the network can only belong to a community at most. The proposed method in the paper is based on the affinity between nodes and it does not agree with the assumption, i.e., the method is able to discover overlapping community structures in networks. We apply the method to various real-world networks and artificial networks, the experimental results confirm that it has better performance on networks with overlapping community structures and non-overlapping community structures.

OComm: Overlapping Community Detection in Multi-View Brain Network.

Many efforts have been made on developing multi-view network community detection approaches. However, most of them can only reveal non-overlapping community structure. In this paper, we propose a novel approach for Overlapping Community Detection in Multi-view Brain Network (oComm). For modeling the overlapping community structure, a community membership strength vector is introduced for each node in each view, based on which a network generative model is designed to measure the within-view community quality. For measuring the consistency of overlapping community structures across different views, the Jaccard similarity is adopted to measure the first-order structural consistency of one node across different views, based on which a cross-view community consistency model is established. One objective function is defined by integrating the above two components. By solving the objective function via the alternative coordinate gradient ascent method, the optimal community membership strength vectors are generated, from which the multi-view overlapping community structure is obtained. Additionally, this study collects a set of EEG data of 147 subjects from Department of Otolaryngology of Sun Yat-sen Memorial Hospital, Sun Yat-sen University, based on which three multi-view brain networks are constructed. Comparison results with several existing approaches have confirmed the effectiveness of the proposed method.

VGHC: a variable granularity hierarchical clustering for community detection

Hierarchical clustering is an effective method for community detection. This kind of method usually selects clustering threshold for layering, which will affect the performance of community detection. Most of them select uniform clustering threshold. It means that the hierarchical structure of the community is obtained by merging synchronously. Actually, the communities are merged asynchronously . So, how to capture the adaptive hierarchical structure of the community is a challenge. In this paper, we propose a variable granularity method VGHC to construct the hierarchical structure based on quotient space theory (QST). QST uses cut set to form a hierarchical structure which represents multi-granular spaces. Firstly, initial granules are built with the important nodes as the granule center. Secondly, we choose the median clustering coefficient of the lower layer as the cut set, and then lower layer can be clustering to higher layer. Finally, the extended modularity (EQ) or modularity (Q) is taken as the evaluation criterion to select a certain layer in the hierarchical structure as the community detection result. Some overlapping nodes exist because of clustering mechanism. To get a non-overlapping community structure, a local modularity optimization approach (LMO) is used to deal with overlapping nodes. Experiments on seven real-world networks demonstrate that our method is effective for community detection in networks compared with the state-of-the-art algorithms.

Immunization of networks with non-overlapping community structure

Although community structure is ubiquitous in complex networks, few works exploit this topological property to control epidemics. In this work, devoted to networks with non-overlapping community structure (i.e., a node belongs to a single community), we propose and investigate three global immunization strategies. In order to characterize the influence of a node, various pieces of information are used such as the number of communities that the node can reach in one hop, the nature of the links (intra-community links, inter-community links), the size of the communities and the interconnection density between communities. Numerical simulations with the susceptible-infected-removed epidemiological model are conducted on both real-world and synthetic networks. Experimental results show that the proposed strategies are more effective than classical alternatives that are agnostic of the community structure. Additionally, they outperform alternative local and global strategies designed for modular networks.

IEDC: An integrated approach for overlapping and non-overlapping community detection

Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains. While there exist many works on community detection based on connectivity structures, they suffer from either considering the overlapping or non-overlapping communities. In this work, we propose a novel approach for general community detection through an integrated framework to extract the overlapping and non-overlapping community structures without assuming prior structural connectivity on networks. Our general framework is based on a primary node based criterion which consists of the internal association degree along with the external association degree. The evaluation of the proposed method is investigated through the extensive simulation experiments and several benchmark real network datasets. The experimental results show that the proposed method outperforms the earlier state-of-the-art algorithms based on the well-known evaluation criteria.

Pinning Control Strategy of Multicommunity Structure Networks

In order to investigate the effects of community structure on synchronization, a pinning control strategy is researched in a class of complex networks with community structure in this paper. A feedback control law is designed based on the network community structure information. The stability condition is given and proved by using Lyapunov stability theory. Our research shows that as to community structure networks, there being a threshold hT≈5, when coupling strength bellows this threshold, the stronger coupling strength corresponds to higher synchronizability; vice versa, the stronger coupling strength brings lower synchronizability. In addition the synchronizability of overlapping and nonoverlapping community structure networks was simulated and analyzed; while the nodes were controlled randomly and intensively, the results show that intensive control strategy is better than the random one. The network will reach synchronization easily when the node with largest betweenness was controlled. Furthermore, four difference networks’ synchronizability, such as Barabási-Albert network, Watts-Strogatz network, Erdös-Rényi network, and community structure network, are simulated; the research shows that the community structure network is more easily synchronized under the same control strength.

Leveraging disjoint communities for detecting overlapping community structure

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network.In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.

Large-scale community detection based on node membership grade and sub-communities integration

Community detection plays an important role in research on network characteristics and in the mining of network information. A variety of algorithms have previously been proposed, but with the continuous growth of network scale, few of them can detect community structure efficiently. Additionally, most of these algorithms only consider non-overlapping community structures in networks. This paper addresses these problems by proposing a new algorithm, based on node membership grade and sub-communities integration, to detect community structure in large-scale networks. The proposed algorithm firstly introduces two functions based on the local information of each node in networks, namely neighboring inter-nodes membership function fMS−NN and node-to-community membership function fMS−NC. Firstly, local potential’s complete sub-graphs are efficiently mined using the function fMS−NN, and then these small graphs are merged into larger ones in light of local modularity. Secondly, incorrectly divided nodes are modified according to function fMS−NN. Additionally, by adjusting the parameters in fMS−NC, we can accurately obtain both non-overlapping communities and overlapping communities. Furthermore, the proposed algorithm employs a framework resembling label propagation, which has low time complexity and is suitable for detecting communities in large-scale networks. Experimental results on both artificial networks and real networks indicate the accuracy and efficiency of the proposed algorithm.

Detecting overlapping communities in networks via dominant label propagation**Project supported by the National Natural Science Foundation of China (Grant Nos. 61173093 and 61202182), the Postdoctoral Science Foundation of China(Grant No. 2012 M521776), the Fundamental Research Funds for the Central Universities of China, the Postdoctoral Science Foundation of Shannxi Province,China, and the Natural Science Basic Research Plan of Shaanxi Province, China

Community detection is an important methodology for understanding the intrinsic structure and function of a real-world network. In this paper, we propose an effective and efficient algorithm, called Dominant Label Propagation Algorithm (Abbreviated as DLPA), to detect communities in complex networks. The algorithm simulates a special voting process to detect overlapping and non-overlapping community structure in complex networks simultaneously. Our algorithm is very efficient, since its computational complexity is almost linear to the number of edges in the network. Experimental results on both real-world and synthetic networks show that our algorithm also possesses high accuracies on detecting community structure in networks.

A Parallel Community Structure Mining Method in Big Social Networks

Community structure plays a key role in analyzing network features and helping people to dig out valuable hidden information. However, how to discover the hidden community structures is one of the biggest challenges in social network analysis, especially when the network size swells to a high level. Infomap is a top-class algorithm in nonoverlapping community structure detection. However, it is designed for single processor. When tackling large networks, its limited scalability makes it less effective in fully utilizing server resources. In this paper, based on infomap, we develop a scalable parallel nonoverlapping community detection method, Pinfomr (parallel Infomap with MapReduce), which utilizes the MapReduce framework to solve the two problems. Experiments on artificial networks and real datasets show that our parallel method has satisfying performance and scalability.

Epidemic spreading on complex networks with overlapping and non-overlapping community structure

Many real-world networks exhibit community structure where vertices belong to one or more communities. Recent studies show that community structure plays an import role in epidemic spreading. In this paper, we investigate how the extent of overlap among communities affects epidemics. In order to experiment on the characteristic of overlapping communities, we propose a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. We simulate the Susceptible-Infected-Susceptible (SIS) epidemic process on synthetic scale-free networks and real-world networks by applying our rewiring algorithm. Experiments show that epidemics spread faster on networks with higher level of overlapping communities. Furthermore, overlapping communities’ effect interacts with the average degree’s effect. Our work further illustrates the important role of overlapping communities in the process of epidemic spreading.

Two-stage algorithm using influence coefficient for detecting the hierarchical, non-overlapping and overlapping community structure

Community detection is one of the most important problems in complex networks. Many algorithms have been proposed in the last decade, and most of them focus on the non-overlapping community structures in the early days. Overlapping and hierarchical structures are another two important properties in complex networks, which have attracted researchers’ extensive concern in recent years. In this paper, we proposed a two-stage method which can detect the hierarchical, non-overlapping and overlapping community structures in complex networks. In this method, the CNM algorithm, a fast hierarchical agglomerative algorithm proposed by Clauset, Newman and Moore, is used in the first stage. In the second stage, a new evaluation function named as influence coefficient based on the local community structure is proposed, which can get the overlapping community structures at different overlapping levels by adjusting a tunable parameter. Besides, the proposed evaluation function can detect the wrongly classified nodes in the partition of the first stage and correct them. Finally, the computational complexity of the algorithm is low. The experimental results on both synthetic and real-world network datasets show the efficiency of our method.

An efficient agent-based algorithm for overlapping community detection using nodes’ closeness

Communities are groups of nodes forming tightly connected units in networks. Some nodes can be shared between different communities of a network. The presence of overlapping nodes and their associated membership diversity is a common characteristic of social networks. Analyzing these overlapping structures can reveal valuable information about the intrinsic features of realistic complex networks, especially social networks.In this paper, we propose a novel algorithm that is able to detect overlapping and non-overlapping community structures in complex networks. This algorithm uses a number of agents for investigation of the input network. These agents consider different nodes’ closeness in their investigations. Various experiments are carried out on both synthetic and real-world networks that prove that the proposed algorithm outperforms most state-of-the-art algorithms of this field both in terms of the accuracy and execution time.

Community structure discovery method based on the Gaussian kernel similarity matrix

Community structure discovery in complex networks is a popular issue, and overlapping community structure discovery in academic research has become one of the hot spots. Based on the Gaussian kernel similarity matrix and spectral bisection, this paper proposes a new community structure discovery method. First, by adjusting the Gaussian kernel parameter to change the scale of similarity, we can find the corresponding non-overlapping community structure when the value of the modularity is the largest relatively. Second, the changes of the Gaussian kernel parameter would lead to the unstable nodes jumping off, so with a slight change in method of non-overlapping community discovery, we can find the overlapping community nodes. Finally, synthetic data, karate club and political books datasets are used to test the proposed method, comparing with some other community discovery methods, to demonstrate the feasibility and effectiveness of this method.

Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex networks

This paper proposes a novel method based on fuzzy clustering to detect community structure in complex networks. In contrast to previous studies, our method does not focus on a graph model, but rather on a fuzzy relation model, which uses the operations of fuzzy relation to replace a traversal search of the graph for identifying community structure. In our method, we first use a fuzzy relation to describe the relation between vertices as well as the similarity in network topology to determine the membership grade of the relation. Then, we transform this fuzzy relation into a fuzzy equivalence relation. Finally, we map the non-overlapping communities as equivalence classes that satisfy a certain equivalence relation. Because most real-world networks are made of overlapping communities (e.g., in social networks, people may belong to multiple communities), we can consider the equivalence classes above as the skeletons of overlapping communities and extend our method by adding vertices to the skeletons to identify overlapping communities. We evaluated our method on artificial networks with built-in communities and real-world networks with known and unknown communities. The experimental results show that our method works well for detecting these communities and gives a new understanding of network division and community formation.