Hierarchical Community Research Articles

Detecting brain network communities: Considering the role of information flow and its different temporal scales

The identification of community structure in graphs continues to attract great interest in several fields. Network neuroscience is particularly concerned with this problem considering the key roles communities play in brain processes and functionality. Most methods used for community detection in brain graphs are based on the maximization of a parameter-dependent modularity function that often obscures the physical meaning and hierarchical organization of the partitions of network nodes. In this work, we present a new method able to detect communities at different scales in a natural, unrestricted way. First, to obtain an estimation of the information flow in the network we release random walkers to freely move over it. The activity of the walkers is separated into oscillatory modes by using empirical mode decomposition. After grouping nodes by their co-occurrence at each time scale, k-modes clustering returns the desired partitions. Our algorithm was first tested on benchmark graphs with favorable performance. Next, it was applied to real and simulated anatomical and/or functional connectomes in the macaque and human brains. We found a clear hierarchical repertoire of community structures in both the anatomical and the functional networks. The observed partitions range from the evident division in two hemispheres –in which all processes are managed globally– to specialized communities seemingly shaped by physical proximity and shared function. Additionally, the spatial scales of a network's community structure (characterized by a measure we term within-communities path length) appear inversely proportional to the oscillatory modes’ average frequencies. The proportionality constant may constitute a network-specific propagation velocity for the information flow. Our results stimulate the research of hierarchical community organization in terms of temporal scales of information flow in the brain network.

Role of Asymptomatic COVID-19 Cases in Viral Transmisssion: Findings from a Hierarchical Community Contact Network Model

Background: As part of on-going efforts to contain the 2019 novel coronavirus disease (COVID-19) pandemic, population-wide screening is being performed, identifying an increasing number of asymptomatic cases. Understanding the role of asymptomatic patients in the transmission system is essential to infection control. However, optimal approach to risk assessment and management of asymptomatic cases remains unclear. Methods: This study involved a SEINRHD epidemic propagation model, constructed based on epidemiological characteristics of COVID-19 in China, accounting for the heterogeneity of social network in this population. Computational experiments were performed to assess epidemic control measures for asymptomatic cases on three dimensions. Impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, type of transmission, and transmission tree structure.Findings: Management of asymptomatic cases can help flatten the infection curve. Specifically, asymptomatic case tracking appears to have significant effect on epidemic progression, whereby tracking 75% of asymptomatic cases corresponds to an overall reduction in new cases of 34·3% (compared to tracking no asymptomatic cases). When timely measures are taken for symptomatic cases and the intensity is strong enough, the overall epidemic is not sensitive to the implementation time of the measures for asymptomatic cases. Finally, regardless of population-wide measures, family transmission is higher than other types of transmission, accounting for an estimated 50% of all new cases.Interpretations: These findings can help assess interventions aimed at asymptomatic case management during the COVID-19 pandemic, helping to control disease spread. In addition, these findings might also help identify the type of transmission and abnormally high transmission events that may exist during the epidemic.Funding: This study was funded by National Natural Science Foundation of China (Nos. 72042018, 91546112, 71621002) and Beijing Municipal Natural Science Foundation (No. L192012).Declaration of Interests: All authors declare no competing interests.

An integrated approach to measure hunting intensity and assess its impacts on mammal populations

Abstract Unsustainable hunting of wildlife is one of the greatest threats to diverse and healthy forests, yet our understanding of hunting activity is limited by our methods of accurately identifying its intensity and distribution. Several methods have been used to quantify hunting in past studies (e.g. interviews, ranger patrols and camera traps). However, none of these alone have been able to produce precise spatiotemporal measures of hunting activity. In this study, we used a new method to detect hunters through passive acoustic monitors and developed an integrated approach to measure hunting activity while simultaneously assessing its impacts on mammal populations using camera traps. We applied a hierarchical community occupancy model that accounted for the imperfect detection of species on data from 45 trap locations, surveyed from January to June 2018, to investigate the impacts of spatial variation and intensity in hunting pressure on mammal species richness and occurrence in four protected areas in southern Belize. We developed spatiotemporally explicit indices of hunting activity separately from camera trap and acoustic monitor data and used a Bayesian model selection framework to identify predictors of site occurrence for individual species and three functional groups: carnivores, herbivores and omnivores. We found that camera traps under‐detected hunting activity in the region by 939% compared to acoustic monitors. Consistent with our predictions, hunting intensity was negatively correlated with site‐level species richness, with an average decrease in richness of 31% across its range of variation. Occurrence patterns for the three functional groups were also negatively associated with hunting intensity. Often the target of hunters, herbivores, displayed the strongest negative response to hunting, while omnivores were least affected. Synthesis and applications. Unsustainable hunting of wildlife is a global phenomenon with wide‐ranging implications for ecological communities, especially mammals. Our study highlights mammal sensitivity to increasing hunting pressure at the community and species level and emphasizes the necessity for developing robust tools to accurately monitor hunting activity, while also providing a flexible framework for simultaneously assessing hunting and its impacts on mammal communities.

Neutral components show a hierarchical community structure in the genotype-phenotype map of RNA secondary structure.

Genotype-phenotype (GP) maps describe the relationship between biological sequences and structural or functional outcomes. They can be represented as networks in which genotypes are the nodes, and one-point mutations between them are the edges. The genotypes that map to the same phenotype form subnetworks consisting of one or multiple disjoint connected components-so-called neutral components (NCs). For the GP map of RNA secondary structure, the NCs have been found to exhibit distinctive network features that can affect the dynamical processes taking place on them. Here, we focus on the community structure of RNA secondary structure NCs. Building on previous findings, we introduce a method to reveal the hierarchical community structure solely from the sequence constraints and composition of the genotypes that form a given NC. Thereby, we obtain modularity values similar to common community detection algorithms, which are much more complex. From this knowledge, we endorse a sampling method that allows a fast exploration of the different communities of a given NC. Furthermore, we introduce a way to estimate the community structure from genotype samples, which is useful when an exhaustive analysis of the NC is not feasible, as is the case for longer sequence lengths.

Hierarchical community structure preserving approach for network embedding

Network embedding aims to map the topological proximities of all nodes in a network into a low-dimensional representation space. Previous studies mainly focus on preserving the within-layer structure of the network (such as first-order proximities, second-order proximities, and community structure). However, many complex networks present a hierarchical organization, often in the form of a hierarchy community structure. How to effectively preserve the within-layer structure and the hierarchical community structure under multi-granularity is a meaningful and still tough task. Inspired by Granular Computing, which is a problem-solving concept deeply rooted in human thinking ability to perceive the real world under multi-granularity, we propose a unified network embedding framework by preserving both the within-layer structure and the hierarchical community structure of the network under multi-granularity, named as Hierarchical Community structure preserving approach for Network Embedding (HCNE). Firstly, different granular networks from fine to coarse are constructed by network granulation which reveals the hierarchical community structure of the original network. Secondly, from coarse to fine, finer networks inherit the embedding of coarse-grained networks as good initialization embedding in the refinement process so that the embedding preserved both the within-layer structure and the hierarchical community structure of the network under multi-granularity. Finally, the learned embedding of each node fed into downstream tasks, including multi-label classification and network visualization. Experimental results demonstrate that HCNE significantly outperforms other state-of-the-art methods. Meanwhile, we intuitively show the effectiveness of HCNE on network visualization which can preserve both the within-layer structure and the hierarchical community structure of the network under multi-granularity.

Encounter Delivery Strategy for Strongly Relational Relay Nodes in MSN

MSN has the characteristics of social network, and the nodes under intellectual consideration show social selfishness, which is characterized by kinship index. We propose an encounter delivery strategy based on strong-relationship relay nodes. Firstly, in the hierarchical communities and clusters based on the kinship index, the active strong relationship node set is obtained through the open strong relationship priority algorithm and Markov prediction. Then establish a kinship trust model, conduct behavior detection on active strong relationship nodes to obtain a comprehensive kinship trust value, and then screen out some of the “false kinship” nodes to screen out a set of trusted active strong relationship nodes. Finally, according to the kinship indication with the destination node, the node’s kinship movement behavior is predicted, and the candidate relay node set is selected. Simulation experiment results show that this strategy can improve the reliability of message transmission, reduce network delay, and improve the efficiency of data packet transmission to a certain extent.

Boundary-connection deletion strategy based method for community detection in complex networks

Community detection in complex networks is a difficult problem. Up to now, there is no very effective method to solve it. Recently, many community detection algorithms based on edge removal have been proposed. However, these edge removal methods often delete many key connections within communities and weaken (or destroy) the community structure of the network. This will make the network communities more difficult to be identified and reduce the accuracy and stability of the algorithm. This paper proposed a boundary connection deletion based community detection algorithm. Different from other algorithms, our algorithm focuses on identifying and removing the boundary connections between network modules. This can enhance the network community structure and get high quality network modules. With high performance, our algorithm can detect the optimal and hierarchical community structure in weighted networks simultaneously. In order to verify the effectiveness of our algorithm, the stability and robustness of our algorithm were firstly analyzed. Then a series of experiments had been done on the real-world and synthetic networks. The real-world networks include Zachary’s karate club network, dolphin social network, American college foot-ball network, PolBooks network, Les Miserables character network, and the coauthorship network of scientists; The synthetic networks include GN benchmark and LFR benckmark. Two indices NMI and Modularity Q were used to compare our algorithm with the recently proposed algorithms, including meta-LPAm+, Srinivas and Rajendran’s model, IDPM, CFCDs, EDCD, CNM, and CDASS. Experimental results show that our algorithm has better performance than these algorithms.

Similarity-Based and Sybil Attack Defended Community Detection for Social Networks

Community detection is a fundamental problem in analyzing the network building principles. Previous community detections left many security issues that every user in a social network must be assigned into a community, which enables attackers easily infiltrate into a community at little cost. For example, an online cancer patients’ community consists solely of cancer patients that want to share drug and treatment information together, but many malicious sybil attackers who collude in joining a target community with ulterior purpose will be probably classified into the target community if one or more patients randomly accept their friendship requests from these attackers. This may lead to the privacy disclosure of community information. Thus, to address this problem, this brief focuses on the novel mechanism of sybil-attack-defended community detection. Firstly, we propose a new similarity-based community detection algorithm (SCDA) which can incorporate any similarity metric to efficiently detect communities and defend sybil attacks in multi-level granularities. Secondly, we are the first to consider both the hierarchical community detection and sybil-attack defense. Several possible sybil-attack models are defined to simulate the attack process in real social networks. Finally, we conduct extensive experiments to demonstrate that our method has a good performance both on community detection and sybil-attack defense.

Exploring Mechanisms of Communication Switching in the Hsp90-Cdc37 Regulatory Complexes with Client Kinases through Allosteric Coupling of Phosphorylation Sites: Perturbation-Based Modeling and Hierarchical Community Analysis of Residue Interaction Networks.

Understanding molecular principles underlying chaperone-based modulation of kinase client activity is critically important to dissect functions and activation mechanisms of many oncogenic proteins. The recent experimental studies have suggested that phosphorylation sites in the Hsp90 and Cdc37 proteins can serve as conformational communication switches of chaperone regulation and kinase interactions. However, a mechanism of allosteric coupling between phosphorylation sites in the Hsp90 and Cdc37 during client binding is poorly understood, and the molecular signatures underpinning specific roles of phosphorylation sites in the Hsp90 regulation remain unknown. In this work, we employed a combination of evolutionary analysis, coarse-grained molecular simulations together with perturbation-based network modeling and scanning of the unbound and bound Hsp90 and Cdc37 structures to quantify allosteric effects of phosphorylation sites and identify unique signatures that are characteristic for communication switches of kinase-specific client binding. By using network-based metrics of the dynamic intercommunity bridgeness and community centrality, we characterize specific signatures of phosphorylation switches involved in allosteric regulation. Through perturbation-based analysis of the dynamic residue interaction networks, we show that mutations of kinase-specific phosphorylation switches can induce long-range effects and lead to a global rewiring of the allosteric network and signal transmission in the Hsp90-Cdc37-kinase complex. We determine a specific group of phosphorylation sites in the Hsp90 where mutations may have a strong detrimental effect on allosteric interaction network, providing insight into the mechanism of phosphorylation-induced communication switching. The results demonstrate that kinase-specific phosphorylation switches of communications in the Hsp90 may be partly predisposed for their regulatory role based on preexisting allosteric propensities.

Hierarchy and the Provision of Order in International Politics

The anarchic international system is actually heavily structured: communities of states join together for common benefit; strong states form hierarchical relationships with weak states to enforce order and achieve preferred outcomes. Breaking from prior research, we conceptualize structures such as community and hierarchy as properties of networks of states’ interactions that can capture unobserved constraints in state behavior, constraints that may reduce conflict. We offer two claims. One, common membership in trade communities pacifies to the extent that breaking trade ties would entail high switching costs: thus, we expect heavy arms trade, more than most types of commercial trade, to reduce intracommunity conflict. Two, this is driven by hierarchical communities in which strong states can use high switching costs as leverage to constrain conflict between weaker states in the community. We find empirical support for these claims using a time-dependent multilayer network model and a new measure of hierarchy based on network centrality.

Bounds on maximum concurrent flow in random bipartite graphs

Algorithms that exploit the duality between maximum concurrent flow and sparse cuts in a graph can effectively discover hierarchical community structures in social networks. We analyze the maximum concurrent flow on random bipartite graphs and compare the results to two bounds based on graph structure.. The minimum degree bound is based on a sparse cut that removes a node of minimum degree. The other is the “gridlock” bound, where all edges are saturated with flow. We show that the gridlock bound is more constraining when the minimum degree of the graph is sufficiently large, and provide a way to calculate this bound on graphs of diameter three, which occurs with high probability on larger random bipartite graphs.

Uncovering hidden community structures in evolving networks based on neighborhood similarity

Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those derived communities as hidden or hierarchical communities. In this work, we present a method called Hidden Community based on Neighborhood Similarity Computation (HCNC) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks.

Network-Based Approaches for Multi-omics Integration.

Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.

Small mammal community composition varies among Ozark glades

Abstract Island biogeography theory (IBT) explains and estimates large-scale ecological patterns among islands and isolated habitat patches. Specifically, IBT predicts that the number of species per habitat patch differs as a function of area and isolation as a result of local colonization and extinction. Accurate estimates of species richness are essential for testing predictions of IBT, but differences in detectability of species can lead to bias in empirical data. Hierarchical community models correct for imperfect detection by leveraging information from across the community to estimate species-specific occupancy and detection probabilities. Using the fragmented Ozark glades as our model system, we constructed a hierarchical community model to 1) estimate site-level and regional species richness of small mammals while correcting for detection error, and 2) determine environmental covariates driving occupancy. We sampled 16 glades in southwestern Missouri in summer 2016–2017 and quantified mammal community structure within the glade network. The detected species pool included eight species, and the model yielded a regional species estimate of 8.6 species, with a mean of 3.47 species per glade. Species richness increased with patch area but not isolation, and effects of patch shape varied between species in the community.

Fast hyperbolic mapping based on the hierarchical community structure in complex networks

The hyperbolic geometry of complex networks has recently emerged as a promising framework for the analysis of real and synthetic networks; one significant challenge of the hyperbolic geometry framework is the necessity to map large real networks to latent geometric spaces. Addressing this problem, we present a novel fast hyperbolic mapping algorithm called hyperbolic mapping based on the hierarchical community structure (HMCS), which is based on the network evolution model, the community-sector model in hyperbolic space and the hierarchical community structure (HCS) of complex networks. We present an index called community closeness (CC) to measure the adjacency relationship between the communities. Then we propose a ranking algorithm for first-level and second-level communities based on CC to determine the order of communities on the hyperbolic disc and map the network into hyperbolic space based on the order and the angular range of the corresponding sector of the second-level communities. We find that HMCS greatly reduces the mapping time complexity and achieves linearity () in sparse networks under the premise of ensuring better mapping accuracy compared with existing hyperbolic mapping algorithms. Experiments show that the HMCS algorithm occupies a unique attractive position in the space of tradeoffs between mapping accuracy and computational complexity.

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.

Differential responses of amphibian and reptile assemblages to size of riparian buffers within managed forests.

Streamside management zones (i.e., riparian buffers; SMZs) are commonly implemented within managed forests to protect water quality but may also provide habitat for riparian-associated wildlife. Yet, little research has rigorously addressed the value of SMZs for wildlife, particularly for cryptic species such as amphibians and reptiles. Previous studies of herpetofauna within SMZs have focused on one or a few stream-associated species, and questions remain regarding variation among species or guilds and what role SMZs serve toward conservation of herpetofaunal diversity in managed forests. However, recent statistical advances have improved our ability to analyze large multi-species presence-absence data sets, accounting for low detection rates typical for some herpetofaunal species. Our study represents an extensive landscape-scale examination of herpetofaunal communities within SMZs using a multi-species occupancy approach. We conducted four replicate surveys at 102 headwater streams, spanning a gradient of SMZ widths and adjacent forest stand ages, within the Ouachita Mountains, Arkansas, USA. We used a hierarchical Bayesian community occupancy model to estimate species richness and species-specific occupancy responses to SMZ and overstory characteristics, accounting for variation in occupancy and detection attributable to site and sampling covariates. We documented high richness (37 species) within SMZs. Across the herpetofaunal community, occupancy and species richness were consistently positively associated with SMZ width, with maximum predicted richness of 30 species occurring at sites with buffers extending 51m on either side of the stream. However, we documented considerable variation among groups and species within groups, underscoring the potential for different responses to forest management among taxa. Reptile predicted richness increased more rapidly up to SMZs of ~35m, whereas maximum salamander predicted richness was not seen until an SMZ width of 55m. Estimated salamander richness was highest within SMZs embedded in mature managed pine stands and was higher in SMZs comprised of a deciduous or mixed overstory vs. a pine overstory. Compared to salamanders, more anuran species showed high mean estimated occupancy (>75%) at narrower SMZs (<30m). Collectively, our results indicate that SMZs surrounding small first-order streams in intensively managed forests not only protect water quality, but also can support diverse amphibian and reptile communities.

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Measuring intrinsic significance of community structure

The significance of communities is an important inherent property of the community structure. It measures the degree of reliability of the community structure identified by the algorithm. Real networks obtained from complex systems always contain error links. Moreover, most of the community detecting algorithms usually involve random factors. Thus evaluating the significance of community structure is very important. In this paper, using the matrix perturbation theory, we propose a normalized index to efficiently evaluate the significance of community structure without detecting communities. Furthermore, we find that the peaks of this index can be used to determine the optimal number of communities and identify hierarchical community structure, which are two challenging problems in many community detecting algorithms. Lastly, the index is applied to 16 typical real networks, and we find that significant community structures exist in many social networks and in the C. elegans neural network. Comparatively insignificant community structures are identified in protein-interaction networks and metabolic networks. Our method can be generalized to broad clustering problems in data mining.

Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree

Hierarchical Community Detection in Social Networks Based on Micro-Community and Minimum Spanning Tree