Non-overlapping Community Detection Research Articles

Urban mobility analytics amid COVID-19 pandemic: A framework for promoting work resumption based on mobile phone data

The Corona Virus Disease 2019 (COVID-19) pandemic had a pernicious influence on the whole world, so that the international community implemented various travel policies to inhibit the viral spread. With the COVID-19 pandemic under control, the overly lenient measures allowed the new variant to take advantage. Also, it is difficult to determine the geographical scope of transport policy that has the least impact on work resumption. Therefore, it is necessary to find policies to promote work resumption while preventing and controlling the pandemic in a geographical perspective. In this paper, a framework based on mobile phone data is proposed for promoting work resumption during the pandemic. First, human mobility networks are constructed through the origin-destination flow extracted from mobile phone data. Second, the singular value decomposition (SVD) algorithm is utilized to recognize the mobility patterns and explore the urban mobility dynamics of work-resumption-related travel during the pandemic. Third, the nonoverlapping community detection algorithm is used to generate policy zoning for work-resumption-related travel. Finally, the policy zoning is validated by public health data based on the overlay analysis and spatial statistics method, and policy implications are proposed. A case study of Shanghai, China is applied to verify the proposed framework. The results show that the framework can capture the mobility patterns and implement the policy zoning based on mobile phone data to balance the economic activity and public health during the pandemic, and the proposed framework can be applied to other cities with future public health events.

Adapt-Infomap: Face clustering with adaptive graph refinement in infomap

Face clustering is a critical task in computer vision due to the increasing number of applications such as augmented reality or photo album management. The primary challenge in this task arises from the imperfections in image feature representations. Given image features extracted from an existing pre-trained representation model, it remains an unresolved problem that how to leverage the inherent characteristics of similarities among unlabelled images to improve the clustering performance. In order to solve face clustering in an unsupervised manner, we develop an effective and robust framework named as Adapt-Infomap. First, we reformulate face clustering as a process of non-overlapping community detection. Specially, Adapt-Infomap achieves face clustering by minimizing the entropy of information flows (also known as the map equation) on an affinity graph of images. Since the affinity graph of images might contain noisy edges, we develop an outlier detection strategy in Adapt-Infomap to adaptively refine the affinity graph. Experiments with ablation studies demonstrate that Adapt-Infomap significantly outperforms existing methods and achieves new state-of-the-arts on three popular large-scale datasets for face clustering, e.g., an absolute improvement of more than 10% and 3% comparing with prior unsupervised and supervised methods respectively in terms of average of Pairwise F-score.

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.

OTUCD: Unsupervised GCN based metagenomics non-overlapping community detection

Metagenomics is a discipline that studies the genetic material of all tiny organisms in the biological environment. In recent years, the interaction between metagenomic microbial communities, the transfer of horizontal genes, and the dynamic changes of microbial ecosystems have attracted more and more attention. It is of great significance to use the community detection algorithm to divide the metagenomic microbes into modules, and it has a positive guiding role for the follow-up research on human, drug, microbial interaction study and drug prediction and development. At present, there are challenges in mining the effective information hidden in large-scale microbial sequence data. The non-linear characteristics and non-scalability of microbial sequence data still bother people. This paper proposes an end-to-end unsupervised GCN learning model OTUCD (Operational Classification Unit Community Detection), which divides large-scale metagenomic sequence data into potential gene modules. We construct an OTU network, and then performs subsequent nonoverlapping community detection task with graph convolutional networks. Experimental scores show that the community detection effect of this method is better than other latest metagenomic algorithms.

MINC-NRL: An Information-Based Approach for Community Detection

Complex networks usually consist of dense-connected cliques, which are defined as communities. A community structure is a reflection of the local characteristics existing in the network topology, this makes community detection become an important research field to reveal the internal structural characteristics of networks. In this article, an information-based community detection approach MINC-NRL is proposed, which can be applied to both overlapping and non-overlapping community detection. MINC-NRL introduces network representation learning (NRL) to represent the target network as vectors, then generates a community evolution process based on these vectors to reduce the search space, and finally, finds the best community partition in this process using mutual information between network and communities (MINC). Experiments on real-world and synthetic data sets verifies the effectiveness of the approach in community detection, both on non-overlapping and overlapping tasks.

Practical and high-quality partitioning algorithm for large-scale and time-evolving graphs

With the appearance of widespread large-scale graph data, distributed graph processing grows in popularity. In order to store and analyze a large-scale graph in a distributed manner, the graph should be properly partitioned in advance. Although the partitioning of static graphs has been sufficiently investigated, the emerging graphs nowadays are typically dynamic or time-evolving. State-of-the-art partitioning algorithms for such large-scale and time-evolving graphs either are impractical because of the complexity caused by the global optimization or sacrifice partitioning quality due to a huge number of cross partition edges. This paper investigates the problem and proposes a new practical and high-quality graph partitioning algorithm. First, graph updates accumulated in a time interval are expressed as a delta graph. Second, vertices in the delta graph are classified into several subsets based on a non-overlapping community detection method. Third, vertices in the same subset are assigned to a properly chosen partition as a whole. The chosen partition is lightly loaded as well as closely related with vertices in the subset. Experimental results show that compared with the widely used hash-based and heuristic-based partitioning algorithms, our proposed algorithm gains significant decrease in terms of the number of cross partition edges while maintaining a similar level of load balance.

Game theoretical approach for non-overlapping community detection

Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes, such as spectral clustering, cannot be applied to a large network due to prohibitive computational complexity required. While there exist methods applicable to large networks, these methods do not offer convincing comparisons against known ground truth. For the first time, this work conducts clustering algorithm performance evaluations on large networks (consisting of one million nodes) with ground truth information. Ideas and concepts from game theory are applied towards graph clustering to formulate a new proposed algorithm, Game Theoretical Approach for Clustering (GTAC). This theoretical framework is shown to be a generalization of both the Label Propagation and Louvain methods, offering an additional means of derivation and analysis. GTAC introduces a tuning parameter which allows variable algorithm performance in accordance with application needs. Experimentation shows that these GTAC algorithms offer scalability and tunability towards big data applications.

Community Detection by Fuzzy Relations

The increasing demand for knowledge from network data poses significant challenges in many tasks. Discovering community structure from a network is one of the classic and significant problems faced in network analysis. In this paper, we study the network structure from the perspective of the composition of fuzzy relations, and a novel algorithm based on fuzzy relations, i.e., CDFR (Community Detection by Fuzzy Relations), is proposed for non-overlapping community detection. The key idea of CDFR is to find the NGC node (Nearest node with Greater Centrality) for each node and compute the fuzzy relation between them. Then, the community to which a node belongs depends on its NGC node. In addition, the decision graph will be constructed to guide community detection. Experimental results on artificial and real-world networks verify the effectiveness and superiority of our CDFR algorithm.

A Novel Community Detection Algorithm Based on Paring, Splitting and Aggregating in Internet of Things

The explosive growth of Internet of Things (i.e., IoT) terminal equipment makes its topology more complex, which leads to the increasing cost of network research. Recently, the implicit community structure is widely used to improve the efficiency of research. However, most of the non-overlapping community detection algorithms have some weakness, such as the large number of community detected and the obvious scale gap between communities. To address these abovementioned problems, we design a novel non-overlapping community detection algorithm, named as Pairing, Splitting and Aggregating algorithm (i.e., PSA). Firstly, in order to improve the accuracy of community division, a new node similarity index is designed to transform the network into a large number of similar node pairs. Secondly, based on the connected branches composed of similar node pairs, the network is further divided into several similar node sets. Thirdly, to balance the scale gap of different communities, the Grasshopper Optimization Algorithm, (i.e., GOA) is introduced to combine the local attribute (i.e., conductance) and global attribute (i.e., modularity) together to aggregate similar node sets into potential (or final) communities. Finally, the experimental results show that PSA not only controls the difference among communities well, but also outperforms the other four popular algorithms in terms of two metrics. Moreover, we propose a community-based resource discovery method (or scheme), named as Community-assisted Short-distance-query Resource Discovery algorithm (i.e., CSRD) to further verify the efficiency of PSA. The results show that the resource discovery efficiency of CSRD using PSA is better compared with other algorithms.

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.

A novel trust-based community detection algorithm used in social networks

Mining the community structure is an important subject in the area of social network analysis, and detecting the hidden communities within the social networks will help to better understand the topological properties of the real-life networks. Meanwhile, community detection will be also helpful to monitor the public opinion, identify the opinion leaders and perform the personalized recommendation. In comparison with the simplex user ties or contents, considering the trust features from multiple users will provide a more comprehensive account of the linking relationship between users. To this end, we propose a novel non-overlapping community detection algorithm, which is based on the trust mechanism, to recognize the community structure in this paper. At first, we propose several definitions with regard to trust relationship between users to depict the trust strength, which includes the direct, indirect and mutual trust, and then the specific trust calculation method is provided to quantitatively describe the extent of trust. Secondly, starting from the trust relationship, we integrate the edge fitness and community fitness into the non-overlapping community detection and propose a novel trust-based algorithm to comprehensively leverage the trust among nodes to further mine the communities within the networks. Finally, to deeply analyze the analyze the performance, we take use of Lesmis and Gemo data sets to carry out extensive experiments, and the results show that, compared with other classical algorithms, the community based on the newly proposed algorithm features the higher trust cohesion on the condition that the structural cohesiveness of social network is fully satisfied. The current methods will be of significance to deeply understand and effectively find out the communities within realistic networks.

A Mixed Representation-Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection.

Designing multiobjective evolutionary algorithms (MOEAs) for community detection in complex networks has attracted much attention of researchers recently. However, most of the existing methods focus on addressing the task of nonoverlapping community detection, where each node must belong to one and only one community. In fact, communities are often overlapped with each other in many real-world networks, thus it is necessary to design overlapping community detection algorithms. To this end, this paper proposes a mixed representation-based MOEA (MR-MOEA) for overlapping community detection. In MR-MOEA, a mixed individual representation scheme is proposed to fast encode and decode the overlapping divisions of complex networks. Specifically, this mixed representation consists of two parts: one represents all potential overlapping nodes and the other delegates all nonoverlapping nodes. These two parts evolve together to detect the overlapping communities of networks based on different updating strategies suggested in MR-MOEA. We verify the effectiveness of the proposed algorithm MR-MOEA on ten real-world complex networks and the experimental results demonstrate that MR-MOEA is superior over six representative algorithms for overlapping community detection.

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.

Applying the Clique Percolation Method to analyzing cross-market branch banking network structure: the case of Illinois

This study applies the Clique Percolation Method (CPM) to an investigation of the changing spatial organization of the Illinois cross-market branch banking network. Nonoverlapping community detection algorithms assign nodes into exclusive communities and, when results are mapped, these techniques may generate spatially disjointed geographical regions, an undesirable characteristic for geographical study. Alternative overlapping community detection algorithms allow overlapping membership where a node can be a member of different communities. Such a structure simultaneously accommodates spatial proximity and spatial separation which occur with respect to a node in relation to other nodes in the system. Applying such a structure in geographical analysis helps preserve well-established principles regarding spatial relationships within the geography discipline. The result can also be mapped for display and correct interpretation. The CPM is chosen in this study due to the complete connection within cliques which simulates the practice by banking institutions of forming highly connected networks through multi-location operations in order to diversify their business and hedge against risks. Applying the CPM helps reveal the spatial pattern of branch banking connections which would otherwise be difficult to see. However, the CPM has been shown to not be among the best performing overlapping community detection algorithms. Future research should explore other possible algorithms for detecting overlapping communities. Detecting communities in a network only reveals certain characteristics of the spatial organization of the network, rather than providing explanation of the spatial-network patterns revealed. Full interpretation of the pattern must rely on the attribute data and additional information. This may illustrate the value of an integrated approach in geographical analysis using both social network analysis and spatial analysis techniques.

On the relationship between Gaussian stochastic blockmodels and label propagation algorithms

The problem of community detection has received great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of the label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark 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.

Efficient overlapping community detection in huge real-world networks

The detection of overlapping community structure in networks can give insight into the structures and functions of many complex systems. In this paper, we propose a simple but efficient overlapping community detection method for very large real-world networks. Taking a high-quality, non-overlapping partition generated by existing, efficient, non-overlapping community detection methods as input, our method identifies overlapping nodes between each pair of connected non-overlapping communities in turn. Through our analysis on modularity, we deduce that, to become an overlapping node without demolishing modularity, nodes should satisfy a specific condition presented in this paper. The proposed algorithm outputs high quality overlapping communities by efficiently identifying overlapping nodes that satisfy the above condition. Experiments on synthetic and real-world networks show that in most cases our method is better than other algorithms either in the quality of results or the computational performance. In some cases, our method is the only one that can produce overlapping communities in the very large real-world networks used in the experiments.