Community Discovery Research Articles

Magnetic-activated cell sorting identifies a unique lung microbiome community

BackgroundThe advent of culture-independent, next-generation DNA sequencing has led to the discovery of distinct lung bacterial communities. Studies of lung microbiome taxonomy often reveal only subtle differences between health and disease, but host recognition and response may distinguish the members of similar bacterial communities in different populations. Magnetic-activated cell sorting has been applied to the gut microbiome to identify the numbers and types of bacteria eliciting a humoral response. We adapted this technique to examine the populations of immunoglobulin-bound bacteria in the lung.MethodsSixty-four individuals underwent bronchoalveolar lavage (BAL). We separated immunoglobulin G-bound bacteria using magnetic-activated cell sorting and sequenced the 16S rRNA gene on the Illumina MiSeq platform. We compared microbial sequencing data in IgG-bound bacterial communities compared to raw BAL then examined the differences in individuals with and without HIV as a representative disease state.ResultsImmunoglobulin G-bound bacteria were identified in all individuals. The community structure differed when compared to raw BAL, and there was a greater abundance of Pseudomonas and fewer oral bacteria in IgG-bound BAL. Examination of IgG-bound communities in individuals with HIV demonstrated the differences in Ig-bound bacteria by HIV status that were not seen in a comparison of raw BAL, and greater numbers of immunoglobulin-bound bacteria were associated with higher pulmonary cytokine levels.ConclusionsWe report a novel application of magnetic-activated cell sorting to identify immunoglobulin G-bound bacteria in the lung. This technique identified distinct bacterial communities which differed in composition from raw bronchoalveolar lavage, revealing the differences not detected by traditional analyses. Cytokine response was also associated with differential immunoglobulin binding of lung bacteria, suggesting the functional importance of these communities.6btME6LTfMPeXYkHvqtMADVideo

Mutual Supervised Fusion & Transfer Learning with Interpretable Linguistic Meaning for Social Data Analytics

Social data analytics is often taken as the most commonly used method for community discovery, product recommendations, knowledge graph, and so on. In this study, social data are firstly represented in different feature spaces by using various feature extraction algorithms. Then we build a transfer learning model to leverage knowledge from multiple feature spaces. During modeling, since the assumption that the training and the testing data have the same distribution is always true, we give a theorem and its proof which asserts the necessary and sufficient condition for achieving a minimum testing error. We also theoretically demonstrate that maximizing the classification error consistency across different feature spaces can improve the classification performance. Additionally, the cluster assumption derived from semi-supervised learning is introduced to enhance knowledge transfer. Finally, aTagaki-Sugeno-Kang (TSK)fuzzy system-based learning algorithm is proposed, which can generate interpretable fuzzy rules. Experimental results not only demonstrate the promising social data classification performance of our proposed approach but also show its interpretability which is missing in many other models.

Overlapping community detection algorithm based on similarity of node relationship

Community discovery is a vital link in the research of social networks aiming at the shortcomings of the current local extension-based community discovery algorithm in local community discovery and extension. In this paper, we proposed a algorithm based on relationship similarity and local extension overlapping community detection (RSLO). First, use the node's relationship similarity strategy to find close seed communities. Then, according to the discovered seed community, the similarity between the neighboring nodes of the community and the community is calculated, and the nodes whose similarity meets the threshold are selected. After that, an adaptive optimization function is used to expand the community. Finally, the free nodes that have not been divided into the community are divided into communities, thereby achieving a more comprehensive community discovery. We conduct experiments on classic datasets and artificially generated networks. The results show that the RSLO algorithm can find accurate and objective community structures.

Research Progress of Complex Network Modeling Methods Based on Uncertainty Theory

A complex network in reality contains a large amount of information, but some information cannot be obtained accurately or is missing due to various reasons. An uncertain complex network is an effective mathematical model to deal with this problem, but its related research is still in its infancy. In order to facilitate the research into uncertainty theory in complex network modeling, this paper summarizes and analyzes the research hotspots of set pair analysis, rough set theory and fuzzy set theory in complex network modeling. This paper firstly introduces three kinds of uncertainty theories: the basic definition of set pair analysis, rough sets and fuzzy sets, as well as their basic theory of modeling in complex networks. Secondly, we aim at the three uncertainty theories and the establishment of specific models. The latest research progress in complex networks is reviewed, and the main application fields of the three uncertainty theories are discussed, respectively: community discovery, link prediction, influence maximization and decision-making problems. Finally, the prospect of the modeling and development of uncertain complex networks is put forward.

Identification of key genes involved in neural regeneration and the repairing effect of BDNF-overexpressed BMSCs on spinal cord ischemia-reperfusion injury in rats

Bone marrow mesenchymal stem cells (BMSCs) can repair spinal cord ischemia-reperfusion injury (SCII); however, only a few BMSCs are usually located in the injured spinal cord. Since the brain-derived neurotrophic factor (BDNF) can promote neural development and maturation, we hypothesised that BDNF-overexpressed BMSCs can ameliorate SCII more effectively than BMSCs alone. To determine the effect of BDNF overexpression on SCII repair, BDNF-overexpressed BMSCs and BMSCs were transplanted into SCII rats. Our results revealed that BDNF-overexpressed BMSCs can better promote the recovery of damaged spinal cords than BMSCs alone. Gene chip detection of spinal cord tissues showed 803 differentially expressed genes in all groups. BTG anti-proliferation factor 2 (Btg2), FOS like 2 (Fosl2), early growth response protein 1 (Egr1), and serpin family E member 1 (Serpine1) were identified as key interrelated genes based on their expression trends, as validated via quantitative PCR and protein–protein interaction network analysis. A co-expression network was constructed to further explore the role of the candidate key genes using Pearson correlation analysis. Cluster 5 was identified as the key cluster using community discovery algorithms. Functional analysis of Cluster 5 genes revealed that this cluster was mainly involved in the stress-activated MAPK cascade, p38MAPK cascade, and apoptosis. Notably, Egr1 may play an important role in SCII repair as the top hub gene in Cluster 5. Therefore, the repair activity of transplanted BDNF-overexpressed BMSCs in SCII rats is better than that of BMSCs alone, which may be regulated by the interactions between Btg2, Fosl2, Egr1, Serpine1, and BDNF.

An Emperical Study on Community Discovery in Complex Networks

An Emperical Study on Community Discovery in Complex Networks

Community Discovery Algorithm Based on Multi-Relationship Embedding

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm.

Explaining causality of node (non-)participation in network communities

Discovering communities in networks constitutes an active and significant research field, especially in real-world networks, where communities usually represent functional units of these networks. In the introduced work, we study the community discovery problem in real-world networks from a different perspective. Our motivation stems from inquiries related to the cause of a node’s participation in a community. Therefore, we incorporate the concepts of causality and responsibility into a novel framework. Our goal is to set up a framework to discover explainable causal relations between nodes and communities, which is a topic that has not been investigated before. We evaluate the proposed framework by experimenting on real-world networks. Moreover, the introduced framework is flexible to be used to other network-related analysis tasks, beyond community detection, such as link prediction, anomaly detection, influence maximization.

Immune mechanism of low bone mineral density caused by ankylosing spondylitis based on bioinformatics and machine learning

Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The module most related to the immune module was defined as the key module. Metascape was used for enrichment analysis of key modules. Further, I-DEGs with the same trend in AS and LBMD were considered key I-DEGs. Multiple machine learning methods were used to construct diagnostic models based on key I-DEGs. IID database was used to find the context of I-DEGs, especially in the skeletal system. Gene-biological process and gene-pathway networks were constructed based on key I-DEGs. In addition, immune infiltration was analyzed on the AS dataset using the CIBERSORT algorithm. Results: A total of 19 genes were identified I-DEGs, of which IFNAR1, PIK3CG, PTGER2, TNF, and CCL3 were considered the key I-DEGs. These key I-DEGs had a good relationship with the hub genes of key modules. Multiple machine learning showed that key I-DEGs, as a signature, had an excellent diagnostic performance in both AS and LBMD, and the SVM model had the highest AUC value. Key I-DEGs were closely linked through bridge genes, especially in the skeletal system. Pathway analysis showed that PIK3CG, IFNAR1, CCL3, and TNF participated in NETs formation through pathways such as the MAPK signaling pathway. Immune infiltration analysis showed neutrophils had the most significant differences between case and control groups and a good correlation with key I-DEG. Conclusion: The key I-DEGs, TNF, CCL3, PIK3CG, PTGER2, and IFNAR1, can be utilized as biomarkers to determine the risk of LBMD in AS patients. They may affect neutrophil infiltration and NETs formation to influence the bone remodeling process in AS.

FirmTruss Community Search in Multilayer Networks

In applications such as biological, social, and transportation networks, interactions between objects span multiple aspects. For accurately modeling such applications, multilayer networks have been proposed. Community search allows for personalized community discovery and has a wide range of applications in large real-world networks. While community search has been widely explored for single-layer graphs, the problem for multilayer graphs has just recently attracted attention. Existing community models in multilayer graphs have several limitations, including disconnectivity, free-rider effect, resolution limits, and inefficiency. To address these limitations, we study the problem of community search over large multilayer graphs. We first introduce FirmTruss , a novel dense structure in multilayer networks, which extends the notion of truss to multilayer graphs. We show that FirmTrusses possess nice structural and computational properties and bring many advantages compared to the existing models. Building on this, we present a new community model based on FirmTruss, called FTCS , and show that finding an FTCS community is NP-hard. We propose two efficient 2-approximation algorithms, and show that no polynomial-time algorithm can have a better approximation guarantee unless P = NP. We propose an index-based method to further improve the efficiency of the algorithms. We then consider attributed multilayer networks and propose a new community model based on network homophily. We show that community search in attributed multilayer graphs is NP-hard and present an effective and efficient approximation algorithm. Experimental studies on real-world graphs with ground-truth communities validate the quality of the solutions we obtain and the efficiency of the proposed algorithms.

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction.

Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction.

The Extraordinarily Rare Ciliate Legendrea loyezae Fauré-Fremiet, 1908 (Haptoria, Ciliophora)

Diverse and dynamic communities of ciliates and other microbes thrive in the natural environment, driving the functioning of aquatic ecosystems. Many microbes are present in very low numbers or are dormant in the 'seedbank', escaping detection in environmental surveys and, consequently, remaining underexplored. Here, we report an extraordinarily rare ciliate that was discovered after persistent exploration of freshwater anoxic sediments - Legendrea loyezae Fauré-Fremiet, 1908, a member of the Family Spathidiidae, Order Haptorida. In this study, we present the sixth account of the ciliate since 1908 and reveal its phylogenetic position with the first 18S rRNA data for the genus. We explain the key morphological features of the species, describing a remarkable behaviour in which the ciliate "shapeshifts'' due to its ability of controlled full extension and retraction of its tube-like tentacles. Our results shed light on the similarity of L. loyezae to another ciliate that was first described as Legendrea bellerophon, later moved under a new genus and named Thysanomorpha bellerophon. We question the validity of this taxonomic decision and, based on morphological characters and tentacle movement, we propose moving T. bellerophon back under Legendrea. This study demonstrates how continued and persistent exploration of natural habitats lead to the discovery of microbial communities and species.

Intentional controlled islanding based on dynamic community detection for power grid

Power system controlled islanding is an emergency control method used to stop the propagation of disturbances and avoid blackouts. Intentional controlled islanding (ICI) has been proposed as a corrective measure of last resort to split the power system into several sustainable islands. Complex network community detection is widely used in network partitioning and can be used to solve controlled islanding problems. The objective of this study is to apply the dynamic network community detection method to solve ICI problems. Using dynamic network modelling, the division-agglomeration (Di-Ag) algorithm is proposed. Dynamic modelling presets the computation task of the algorithm in the healthy grid phase. The algorithm balances the efficiency of the algorithm, power imbalance, and power flow disruption, without sacrificing other objectives to optimise one objective. Specifically, the grid topology is taken into account when the grid is divided using betweenness as edge weight. The first step of the Di-Ag algorithm uses the dynamic Girvan–Newman algorithm to achieve the goal of power imbalance. Moreover, only the islands' involved lines (nodes) are updated, which improves the efficiency of the algorithm. In the second step of the Di-Ag algorithm, the algorithm merges the islands to reduce power flow disruption. The IEEE 39-bus and 118-bus system are used to compare the performance of the proposed dynamic modelling method and the static network community discovery method in three different cases.

Attributed community search based on seed replacement and joint random walk

Community search enables personalized community discovery and has wide applications in real-life scenarios. Existing attributed community search algorithms use personalized information provided by attributes to locate desired community. Though achieved promising results, existing works suffer from two major limitations: (i) the precision of the algorithm decreases significantly when the seed comes from the boundary regions of the community. (ii) Most attributed community search methods mainly take the attribute information as edge weights to reveal semantic strength (e.g., attribute similarity, attribute distance, etc.), but largely ignore that attribute may serve as heterogeneous vertex. To make up for these deficiencies, in this paper, we propose a novel two-stage attributed community search method with seed replacement and joint random walk (SRRW). Specifically, in the seed replacement stage, we replace the initial query node with a core node; in the random walk stage, attributes are taken as heterogeneous nodes and the augmented graph is modeled based on the affiliation of the attributes via an overlapping clustering algorithm. And finally, a joint random walk is performed on the augmented graph to explore the desired local community. We conduct extensive experiments on both synthetic and real-world benchmarks, demonstrating its effectiveness for attributed community search.

Time series clustering based on complex network with synchronous matching states

Due to the extensive existence of time series in various fields, more and more research on time series data mining, especially time series clustering, has been done in recent years. Clustering technology can extract valuable information and potential patterns from time series data. This paper proposes a time series Clustering method based on Synchronous matching of Complex networks (CSC). This method uses density peak clustering algorithm to identify the state of each time point and obtains the state sequence according to the timeline of the original time series. State sequences is a new method to represent time series. By comparing two state sequences synchronously, the length of state sequence with step is calculated and the similarity is presented, which forms a new method to calculate the similarity of time series. Based on the obtained time series similarity, the relationship network of time series is constructed. Simultaneously, the community discovery technology is applied to cluster the relationship network and further achieve the complete time series clustering. The detailed process and simulation experiments of CSC method are given. Experimental results on different datasets show that CSC method is superior to other traditional time series clustering methods.

How to Protect Ourselves From Overlapping Community Detection in Social Networks

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question “Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?” If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called name <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BIH</i> . Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.

Identification of Communities With Multi-Semantics via Bayesian Generative Model

Discovering communities is an essential step in the analysis of complex systems, and it has two purposes: to identify functional modules and to interpret semantics. However, most of the existing community detection methods only focused on identify communities, while learning the semantics interpretation of communities has not been fully studied. In this paper, we focused on the problem of identifying communities and learning the semantics interpretation of modules jointly in an end-to-end model. We designed a novel generative model which combines two closely related parts, one for community discovery and the other for content clustering and semantics interpretation. By extracting the potential correlation between these two parts, our new method is not only robust to discovering communities, but also able to provide a community with more than one semantic topic. As for model inference, we developed a variational algorithm from a Bayesian point of view. Experimental results on the artificial benchmark and real networks showed the superior performance of the proposed approach over existing methods in terms of effectiveness and efficiency. We also analyzed semantic interpretability of community detection results through a case study over a large-scale music platform dataset.

20 years of network community detection

A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles. In this commentary we review progress in the field over the last 20 years.

Analysis of Key Factors of College Students’ Ideological and Political Education Based on Complex Network

Rapid updating and complex network means bringing more development opportunities for the education industry. As for ideological classes and political science at colleges and universities, how to use sophisticated online technology to educate and educate ideological and political classes and improve the formation of ideological and political classes has been a complex theory in the education industry in recent years. Complex network is an abstract representation of complex systems in the real world, with broad research value and application prospects, and has many advantages in complex network research, with interpretability, expression ability, generalization ability, flexibility, etc., and has been used in various network analysis tasks, such as community discovery, link prediction, network representation, and political learning. The second part of this article focuses on (1) the concept of ideological education, (2) the advantages of ideological education, (3) the contradiction of the mode of ideological education, and (4) how to innovate ideological education. The third part proposes the basic characteristics and models of complex networks and proposes the emergence of fractal structures. The fourth part analyzes complex network models in detail and compares the number of points and sides of the real network with the respective irregular networks, indicating that the real world is not fully defined or completely irregular, and that the real network has the nature of a small world and a high-quality cluster. The key factors of complex networks applied propose theoretical and political education and ultimately influence theoretical and political teachers and students in the complex networks examined through empirical questionnaires. He proposes that large universities should be able to use a network environment to promote ideological and political education.