Overlapping Community Detection Method Research Articles

Evolutionary multi-objective overlapping community detection based on fusion of internal and external connectivity and correction of node intimacy

In the field of community detection, node attribute information plays an important role in community division. Existing methods use topology structure and node attribute information to discover non-overlapping communities. However, so far, attribute information has not been fully utilized in overlapping community detection. To address this, we propose a new overlapping community detection method called “evolutionary multi-objective overlapping community detection based on Fusion of internal and external Connectivity and Correction of Node Intimacy” (FCCNI). Firstly, we propose a fusion strategy based on internal and external connectivity, which integrates some communities with sparse intra-connections and dense inter-connections. This automatically determines, reconfirms, and corrects the number of communities. Secondly, a function is designed to calculate the intimacy between nodes, and the node label with the highest intimacy is selected to correct the current wrong node. The correction strategy is used in two stages of initialization and multi-objective evolution to obtain a more accurate node label. Finally, a method that considers not only the connections of the community, but also the node attribute, is designed to obtain the overlapping community indirectly from the non-overlapping community. The experimental results on five real-life networks and four classical synthetic networks show that FCCNI achieves better overlapping community division, compared with six state-of-the-art comparison algorithms from the literature.

A hierarchical overlapping community detection method based on closed trail distance and maximal cliques

An important feature of real networks is their hierarchy and the existence of overlapping communities. Hierarchical agglomerative clustering is one way to determine the hierarchy of a network. To ensure the existence of overlapping communities, it is appropriate to choose the base elements for clustering – edges, cliques, etc. These base elements can then have common vertices and naturally provide the possibility of overlap. The proposed community detection method uses hierarchical agglomerative clustering on the 2-edge-connected component of the graph. Communities are constructed from maximal cliques as base elements. Novel dissimilarities for hierarchical agglomerative clustering were introduced for the merging of cliques. The dissimilarities use the size of the overlapped cliques and closed trail distance to express dissimilarity between communities in networks. The single linkage approach contains and extends the results of k-CPM. The proposed algorithm utilizing deterministic dissimilarity achieves comparable or superior outcomes compared to standard algorithms used for hierarchical or overlapping community detection.

Analysis of the Pertinence of Indian Women’s Institutions in Collaborative Research

Nowadays, the relevance of women-only educational institutes is questionable due to various reasons that include social uplifting, ample opportunities, and a free mindset among the population. Though women colleges and universities bring many female students to the purview of the traditional education system every year, other activities like research works and collaborative projects are still a concern for the educationists. In the current manuscript, the role of leading women-only universities and institutions is analyzed throughout India, to scrutinize its impact and opportunities in the research domain. All types of educational institutions and collaborative research work among them are represented as a social network. Different centrality metrics, such as closeness, betweenness, and eigenvector, are utilized to examine the influence of the individual institutes in the collaborative research network. An overlapping community detection method is proposed to perceive the posture of gender-biased universities in the mixed institutional model. The supremacy of the proposed approach is presented over the real-life benchmark datasets. The analytical results exhibit notable improvement over the state-of-the-art approaches and unfold a contemporary framework for strategic analysis in the higher education sector.

Overlapping community detection using expansion with contraction

Numerous disjoint community detection methods have reached the state-of-the-art. Some overlapping community detection methods have been proposed in recent years, but they lack the ability to adjust the degree of overlap while maintaining detection quality. To well handle this issue, we in this paper propose a novel method, namely expansion with contraction method for overlapping community detection (ECOCD). Specifically, ECOCD obtains the disjoint communities through non-negative matrix factorization and proceeds to expansion with contraction process (including the expansion process and the contraction process). In each iteration of the process, we randomly select a community and then continuously conduct the expansion and contraction processes on this community. The former process absorbs nodes by the degree of affiliation that is newly defined, while the latter removes nodes by permanence. Moreover, we theoretically analyze the computational complexity of ECOCD. The advantage of ECOCD is that it is applicable to various networks with different properties by adjusting the degree of overlap, and enjoys high quality of overlapping community detection as well. Our experiments on both synthetic and real-world networks further verify this. Extensive experiments show that ECOCD is superior to the eleven state-of-the-art overlapping community detection methods in terms of four metrics, validating the effectiveness, efficiency and robustness of ECOCD.

An effective overlapping community merging method oriented to multidimensional attribute social networks

AbstractFor multidimensional attribute social networks, most of the existing methods have limitations in dealing with community redundancy, namely, the influences of the network's multidimensional attributes and users' interest preferences are ignored in the process of community merging. To this end, we propose an effective overlapping community merging method (EOCMM) oriented to multidimensional attribute social networks. In EOCMM, we focus on two core problems which are how to improve the method of improving overlapping community detection in multidimensional attribute social networks and how to merge the detected communities with high redundancy degree. To solve the first problem, based on the network topology characteristics and user interest preference, the Node Domain index is proposed to improve and optimize the selection of seed nodes. The traditional overlapping community detection method is improved by semantic fitness function and seed nodes. To solve the second problem, we merge the communities with high redundancy degree by Semantic Community Overlapping Degree which is fused by user similarity, community similarity and community tightness. Finally, we compared our method with other multiple mainstream methods by extensive experiments on three real social network datasets. The experimental comparison results show that the improvements on three evaluation metrics extension of modularity, partition density and semantic extension of modularity are 6.04%, 7.96% and 3.13%, respectively. And our method can make the results of community detection and merging more reasonable and effective.

Detecting susceptible communities and individuals in hospital contact networks: a model based on social network analysis

In the real world, the spread of various epidemics can be modelled using the SEIR disease transmission model. Early detection and prevention of susceptible individuals is an effective method of controlling the spread of infectious viruses. This paper presents a study that detects susceptible community on a hospital contact network, which encompasses patients, nurses, doctors and managers. The goal of our work is to identify susceptible communities with patients and healthcare workers, and analyse the independent contact networks of various roles to determine the high-influence nodes within the hospital contact network. If these high-influence nodes are part of the susceptible community, they should be the focus of observation to prevent the spread of the virus. The proposed model combines social network analysis method and machine learning method with the disease transmission model. This study employs the classic overlapping community detection method CPM for community detection and the PageRank algorithm to rank node influence. The experimental results, obtained from real-world hospital contact networks over a 4-day period, demonstrate the effectiveness of the model.

Multidimensional empirical analysis of overlapping community detection methods in social networks

Multidimensional empirical analysis of overlapping community detection methods in social networks

ISOCD: Overlapping community detection algorithm based on local influence and similarity

Many nodes of a complex network in the real world belong to multiple communities. Research on the detection of overlapping communities with common nodes can help us understand the topology of network and extract information hidden in the network. Considering the problems of many existing overlapping community detection methods, such as high time complexity or poor division results, we propose an overlapping community detection method based on local influence and similarity (ISOCD). The ISOCD method selects seed nodes based on local influence, follows a greedy strategy, takes the fitness function of similarity as the optimization goal, and finally optimizes and merges the detected communities. In experiments, the method we propose has good community division effects both on the real-world and synthetic networks, and has low time complexity.

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.

Community detection in multi-relational directional networks

Community detection algorithms are essential tools that allow us to discover organizational principles in networks. Today, in most cases, social networks are modeled as a multi-relational network. So far, despite the introduction of community detection algorithms specific to multi-relational networks, very few of these algorithms have been able to find overlapping communities in a multi-relational directional network. In this paper, we present OCMRN (Overlapping Communities in Multi-Relational Networks), an overlapping community detection method in multi-relational directional networks. A semi-supervised method is used in the training phase of this algorithm which allows determining the importance of each layer to shape communities. The proposed method is evaluated on nine real and eight synthetic datasets and is compared with different algorithms. Well-known evaluation criteria are used to compare overlapping and non-overlapping communities resulting from various algorithms. The high accuracy of the results obtained from the proposed model suggests this algorithm can be used with high confidence for community detection of multi-relational directional networks to find overlapping communities.

Overlapping community detection on complex networks with Graph Convolutional Networks

Discovering the community structure within networks is of significance with respect to many realistic applications, like recommendation systems and cyberattack detection. In this study, we propose an overlapping community detection method CDMG based on Graph Convolutional Network (GCN) from the perspective of maximizing the Markov stability of community structure, which is defined in terms of the clustered autocovariance of a Markov process taking place on the network. Extensive experiments on both the attributed networks and the normal networks with different scales demonstrate the superiority of CDMG compared to other established community detection algorithms. Additionally, the Markov stability of the community structure relies on a time parameter, Markov time, and we observe that the performance of CDMG can be further improved by utilizing the optimal Markov time. According to the variation curve that demonstrates the influence of Markov time t on the performance of CDMG, we propose a trichotomy-based method to search for the optimal Markov time for our method.

The recognition of kernel research team

• The definition of local mutual degree of dependence : Inspired by Adams’s fourth elements and Axiomatic index, a local index is proposed to measure the relationships between any pair of co-authors. In contrast to node filtration through the fixed threshold, our algorithm automatically distinguishes the kernel authors; the iteration converges quickly; the output network contains a small number of authors who have published plenty of papers and get much attention. • The presence of Pareto principle in science : The statistics of co-author networks from ten research fields show that a small number of authors published the most papers and achieved the most citations in the networks, indicating the presence of the Pareto principle in co-authorship networks. • Kernel research team recognized : A fast attributed overlapping community detection method is applied to the academic networks for detecting kernel research teams. The degree distributions of these kernel teams are uniform flat than that of the communities detected by original co-authorship networks. Scientific projects are usually created by teams rather than individuals since the realizations of the projects need complex instruments and multidisciplinary cooperations. Although there is a myriad of reports on the assembly mechanisms of research teams, most are restricted to the empirical analysis of some special teams, and they failed to analyze the research team from big co-authorship networks. Inspired by L. G. Adams’s “basic elements” of the successful research team, this paper proposed a method for identifying the kernel research teams from the co-author networks. We create a database containing all articles published in the journals recommended by the China Computer Federation (CCF), based on which the networks of ten subfields in computer science are constructed. In the empirical analysis, a handful of scholars are found to contribute a large portion of literature and gather numerous citations; this proves the presence of the Pareto principle in academic networks. Furthermore, the information of 34 famous research teams is collected and analyzed; our study shows most leaders and members who belong to these 34 teams can be recovered from the network of kernel research teams even when more than 70 % of authors are removed from the original co-authorship network. Finally, in order to take full advantage of the authors’ research interests, we improve the original label propagation method to guarantee good performance in our dataset.

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

Community detection in attributed graph is of great application value and many related methods have been continually presented. However, existing methods for community detection in attributed graph still cannot well solve three key problems simultaneously: link information and attribute information fusion, prior information integration and overlapping community detection. Aiming at these problems, in this paper we devise a semi-supervised overlapping community detection method named SSGCAE which is based on graph neural networks. This method is composed of three modules: graph convolutional autoencoder (GCAE), semi-supervision and modularity maximization, which are respectively utilized to fuse link information and attribute information, integrate prior information and detect overlapping communities. We treat GCAE as the backbone framework and train it by using the unified loss from these three modules. Through this way, these three modules are jointly correlated via the community membership representation, which is very beneficial to improve the overall performance. SSGCAE is comprehensively evaluated on synthetic and real attributed graphs, and experiment results show that it is very effective and outperforms state-of-the-art baseline approaches.

Delegation Mechanism-Based Large-Scale Group Decision Making With Heterogeneous Experts and Overlapping Communities

The widespread utilization of novel technological and societal paradigms gave birth to the development large-scale group decision making. This study focuses on two issues regarding the structure of the large group. The first is about the overlapping clusters/communities (interdependent subgroups) of large-scale experts identified from the dimension reduction process. The second is regarding the situation in which internal and external experts (i.e., heterogeneous experts) exist in a large-scale group decision-making problem simultaneously. To manage the first issue, we propose to use an existing overlapping community detection method to cluster experts considering the independencies of communities. To address the second issue, a delegation mechanism is introduced to allocate the trust weights of external experts to internal experts. Furthermore, to reach a consensus for large-scale experts, a consensus measure based on two reference points (global collective preference and community preference) is given. Finally, an illustrative example regarding 5G technology investment is provided to verify the applicability of the proposed model.

Quadratic Optimization based Clique Expansion for overlapping community detection

Community detection is a crucial task in the field of network analysis. A community is a collection of tightly connected nodes only have sporadic external connections. In many real-world networks, communities naturally overlap, and prior knowledge about them is usually unavailable, such as the number of ground-truth communities in the network. In this work, we present the QOCE (Quadratic Optimization based Clique Expansion), an overlapping community detection method that does not require any prior knowledge. QOCE follows the popular seed set expansion strategy and regards each high-quality maximal clique as the initial seed set. For seed set expansion, QOCE uses a fast short random walk to sample a subgraph from a clique seed set, then adopts a quadratic optimization to approximate the Cheeger cut on the sampled subgraph. Finally, a local minimum of conductance determines the boundary of the community. We extensively evaluate our method by comparing it with four state-of-the-art baseline algorithms on synthetic and real-world networks in various domains and scales. Empirical results demonstrate the competitive performance of our method in terms of detection accuracy and efficiency.

SLPA-IF1: Label Propagation Based Overlapping Community Detection

Detection of overlapping communities over a network is imperative due to its applicability in multiple domains starting from geographical to online networks. This paper proposes an effective overlapping community detection method SLPA-IF1. Initially, nodes label initialization is done during pre-processing of data. Label updation and propagation is performed during the evolution phase which consists of selection of listener node, speaker rule and listener rule. Speaker rule is modified to consider the mean of occurring frequency of labels instead of random label selection. We have also proposed a new measure named label specificity for listener rule which is calculated as the mean of occurring frequency of labels minus probability of occurrence of that label. The proposed method leads to more accurate label selection during detection of communities over a network. The run time computation has shown the scalability of the proposed method with respect to increasing network size. For large scale networks, the computing time of the proposed method is less than other state-of-the-art methods.

Social influence-based personal latent factors learning for effective recommendation

Social recommendation has become an important technique of various online commerce platforms, which aims to predict the user preference based on the social network and the interactive network. Social recommendation, which can naturally integrate social information and interactive structure, has been demonstrated to be powerful in solving data sparsity and cold-start problems. Although some of the existing methods have been proven effective, the following two insights are often neglected. First, except for the explicit connections, social information contains implicit connections, e.g., indirect social relations. Indirect social relations can effectively improve the quality of recommendation when users only have few direct social relations. Second, the strength of social influence between users is different. In other words, users have different degrees of trust in different friends. These insights motivate us to propose a novel social recommendation model SIER (short for Social Influence-based Effective Recommendation) in this paper, which incorporates interactive information and social information into personal latent factors learning for social influence-based recommendation. Specifically, user preferences are captured in behavior history and social relations, i.e., user latent factors are shared in interactive network and social network. In particular, we utilize an overlapping community detection method to sufficiently capture the implicit relations in the social network. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

Sparsity of weighted networks: Measures and applications

A majority of real life networks are weighted and sparse. The present article aims at characterization of weighted networks based on sparsity, as an indicator of inherent diversity of different network parameters. The measure called sparsity index defined on ordered degree sequence of simple networks is extended and new properties of this index are derived. The range of possible values of sparsity index of any connected network, with edge-count in specific intervals, is worked out analytically in terms of node-count and a pattern is uncovered in corresponding degree sequences. Given the edge-weight frequency distribution of a network, an expression of the sparsity index of edge-weights is formulated. Its properties are analyzed under different distributions of edge-weights. For example, the upper and lower bounds of sparsity index of edge-weights of a network, with all distinct edge-weights, is determined in terms of its node-count and edge-density. The article highlights that this summary index with low computational cost, computed on different network parameters, is useful to reveal different structural and organizational aspects of networks for performing analysis. An application of this index is demonstrated through devising a new overlapping community detection method. The results validated on artificial and real-world networks show its efficacy.

Uncovering local community structure on line graph through degree centrality and expansion

Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.

PODCD: Probabilistic overlapping dynamic community detection

Community detection is an important task to reveal hidden structures of real-world complex networks which are vary over time. Most of the existing works on the dynamic community detection assumes the sparse connectivity between communities and supposes that the number of nodes and communities in different snapshots is constant. In this work, a probabilistic overlapping community detection method called PODCD is proposed that considers the task of detecting communities as a non-negative matrix factorization problem. The proposed method considers the more likely assumption of dense connections between communities and utilizes a probabilistic model to control the dynamics of community structure. The proposed method uses the block coordinate decent method to solve the objective function of the matrix factorization model. This solver estimates non-negative latent factor to speeds up the computation of gradients. We demonstrate the efficiency of the proposed method by performing experiments on several synthetic and real-world dynamic networks. The obtained results reveal that the proposed method outperforms the earlier algorithms on evolving networks in terms of well-known evaluation criteria.