Node Centrality Measures Research Articles

Dynamical network-based evaluation for neuromuscular dysfunction in stroke-induced hemiplegia during standing

BackgroundA given movement requires precise coordination of multiple muscles under the control of center nervous system. However, detailed knowledge about the changing characteristics of neuromuscular control for multi-muscle coordination in post-stroke hemiplegic patients during standing is still lacking. This study aimed to investigate the hemiplegia-linked neuromuscular dysfunction during standing from the perspective of multi-muscle dynamical coordination by utilizing a novel network approach – weighted recurrence network (WRN).MethodsTen male hemiplegic patients with first-ever stroke and 10 age-matched healthy male adults were instructed to stand on a platform quietly for 30 s with eyes opened and eyes closed, respectively. The WRN was constructed based on the surface electromyography signals of 16 muscles from trunk, hips, thighs and calves. Relevant topological parameters, including clustering coefficient (C) and average shortest path length (L), were extracted to evaluate the dynamical coordination of multiple muscles. A measure of node centrality in network theory, degree of centrality (DC), was innovatively introduced to assess the contribution of single muscle in the multi-muscle dynamical coordination. The standing-related assessment metric, center of pressure (COP), was provided by the platform directly.ResultsResults showed that the post-stroke hemiplegic patients stood with remarkably higher similarity of muscle activation and more coupled intermuscular dynamics, characterized by higher C and lower L than the healthy subjects (p < 0.05). The DC values and rankings of back, hip and calf muscles on the affected side were significantly decreased, whereas those on the unaffected side were significantly increased in hemiplegia group compared with the healthy group (p < 0.05). Without visual feedback, subjects exhibited enhanced muscle coordination and increased muscle involvement (p < 0.05). A decrease in C and an increase in L of WRN were observed with decreased COP areas (p < 0.05).ConclusionsThese findings revealed that stroke-induced hemiplegia could significantly influence the neuromuscular control, which was manifested as more coupled intermuscular dynamics, abnormal deactivation of muscles on affected side and compensation of muscles on unaffected side from the perspective of multi-muscle coordination. Enhanced multi-muscle dynamical coordination was strongly associated with impaired postural control. This study provides a novel analytical tool for evaluation of neuromuscular dysfunction and specification of responsible muscles for impaired postural control in stroke-induced hemiplegic patients, and could be potentially applied in clinical practice.

Construction of Metaphorical Maps of Cyberspace Resources Based on Point‐Cluster Feature Generalization

ABSTRACTIn the digital age, the expansion of cyberspace has resulted in increasing complexity, making clear cyberspace visualization crucial for effective analysis and decision‐making. Current cyberspace visualizations are overly complex and fail to accurately reflect node importance. To address the challenge of complex cyberspace visualization, this study introduces the integrated centrality metric (ICM) for constructing a metaphorical map that accurately reflects node importance. The ICM, a novel node centrality measure, demonstrates superior accuracy in identifying key nodes compared to degree centrality (DC), k‐shell centrality (KC), and PageRank values. Through community partitioning and point‐cluster feature generalization, we extract a network's hierarchical structure to intuitively represent its community and backbone topology, and we construct a metaphorical map that offers a clear visualization of cyberspace. Experiments were conducted on four original networks and their extracted backbone networks to identify core nodes. The Jaccard coefficient was calculated considering the results of the three aforementioned centrality measures, ICM, and the SIR model. The results indicate that ICM achieved the best performance in both the original networks and all extracted backbone networks. This demonstrates that ICM can more precisely evaluate node importance, thereby facilitating the construction of metaphorical maps. Moreover, the proposed metaphorical map is more convenient than traditional topological maps for quickly comprehending the complex characteristics of networks.

Using network analysis to provide evidence for brain health as a unified construct relevant to aging with HIV.

Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV. Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network. The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops," indicating strong connectivity. The most central symptoms were "How much do you enjoy life?" and "How often do you have negative feelings?". The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.

The Impact of Centrality Measures in Protein–Protein Interaction Networks: Tools, Databases, Challenges and Future Directions

Analyzing protein–protein interaction (PPI) networks using machine learning and deep learning algorithms, alongside centrality measures, holds paramount importance in understanding complex biological systems. These advanced computational techniques enable the extraction of valuable insights from intricate network structures, shedding light on the functional relationships between proteins. By leveraging AI-driven approaches, researchers can uncover key regulatory mechanisms, identify critical nodes within the network and predict novel protein interactions with high accuracy. Ultimately, this integration of computational methodologies enhances our ability to comprehend the dynamic behavior of biological systems at a molecular level, paving the way for advancements in drug discovery, disease understanding and personalized medicine. This review paper starts by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. It primarily delves into the centrality-driven discoveries within PPI networks in biological systems and suggests using edge centrality measures and a hybrid version of node and edge centrality measures in machine learning algorithms and deep learning algorithms to predict hidden knowledge much more effectively.

A parameterised model for link prediction using node centrality and similarity measure based on graph embedding

Link prediction is a crucial aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves the prediction of potential new links between nodes within a network. Despite its importance, current models for link prediction exhibit notable limitations. Graph Convolutional Networks have shown high efficiency in link prediction across various datasets. However, they face significant challenges when applied to short-path networks and ego networks, resulting in poor performance. This issue represents a critical area of concern that our work seeks to address. This paper introduces the Node Centrality and Similarity-based Parameterised Model (NCSM), a novel method for link prediction tasks. NCSM uniquely integrates node centrality and similarity measures as edge features in a customised Graph Neural Network (GNN) layer, effectively leveraging the topological information of large networks. This model represents the first parameterised GNN-based link prediction model that considers topological information. The proposed model was evaluated on five benchmark graph datasets, each comprising thousands of nodes and edges. Experimental results highlight NCSM's superiority over existing state-of-the-art models like Graph Convolutional Networks and Variational Graph Autoencoder, as it outperforms them across various metrics and datasets. This exceptional performance can be attributed to NCSM's innovative integration of node centrality, similarity measures, and its efficient use of topological information.

Local control: a hub-based model for the c-di-GMP network.

The genome of Pseudomonas fluorescens encodes >50 proteins predicted to play a role in bis-(3'-5')-cyclic dimeric guanosine monophosphate (c-di-GMP)-mediated biofilm formation. We built a network representation of protein-protein interactions and extracted key information via multidimensional scaling (i.e., principal component analysis) of node centrality measures, which measure features of proteins in a network. Proteins of different domain types (diguanylate cyclase, dual domain, phosphodiesterase, PilZ) exhibit unique network behavior and can be accurately classified by their network centrality values (i.e., roles in the network). The predictive power of protein-protein interactions in biofilm formation indicates the possibility of localized pools of c-di-GMP. A regression model showed a statistically significant impact of protein-protein interactions on the extent of biofilm formation in various environments. These results highlight the importance of a localized c-di-GMP signaling, extend our understanding of signaling by this second messenger beyond the current "Bow-tie Model," support a newly proposed "Hub Model," and suggest future avenues of investigation.

Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, and temporal supernetworks can capture the dynamic evolution of these systems. From the perspective of network stability, supernetworks can aid in risk identification for complex systems. In this paper, an IO-SuperPageRank algorithm is proposed based on the supernetwork topological structure. This algorithm reveals network instability by calculating changes in node importance, thereby helping to identify risks in complex systems. To validate the effectiveness of this algorithm, a four-layer supernetwork composed of scale-free networks is constructed. Simulated experiments are conducted to assess the impact of changes in intralayer edge numbers, intralayer node numbers, and interlayer superedge numbers on the risk indicator IO value. Linear regression and multiple tests were used to validate these relationships. The experiments show that changes in the three network topological indicators all bring about risks, with changes in intralayer node numbers having the most significant correlation with the risk indicator IO value. Compared to traditional measures of network node centrality and connectivity, this algorithm can more accurately predict the impact of node updates on network stability. Additionally, this paper collected trade data for crude oil, chemical light oil, man-made filaments and man-made staple fibers from the UN Comtrade Database. We constructed a man-made filaments and fibers supply chain temporal supernetwork, utilizing the algorithm to identify supply chain risks from December 2020 to October 2023. The study revealed that the algorithm effectively identified risks brought about by changes in international situations such as the Russia-Ukraine war, Israel–Hamas conflict, and the COVID-19 pandemic. This demonstrated the algorithm’s effectiveness in empirical analysis. In the future, we plan to further expand its application based on different scenarios, assess risks by analyzing changes in specific system elements, and implement effective risk intervention measures.

Network pharmacology study to explore the multiple molecular mechanism of SH003 in the treatment of non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is one of the leading causes of human death worldwide. Herbal prescription SH003 has been developed to treat several cancers including NSCLC. Due to the multi-component nature of SH003 with multiple targets and pathways, a network pharmacology study was conducted to analyze its active compounds, potential targets, and pathways for the treatment of NSCLC.MethodsWe systematically identified oral active compounds within SH003, employing ADME criteria-based screening from TM-MC, OASIS, and TCMSP databases. Concurrently, SH003-related and NSCLC-associated targets were amalgamated from various databases. Overlapping targets were deemed anti-NSCLC entities of SH003. Protein–protein interaction networks were constructed using the STRING database, allowing the identification of pivotal proteins through node centrality measures. Empirical validation was pursued through LC–MS analysis of active compounds. Additionally, in vitro experiments, such as MTT cell viability assays and western blot analyses, were conducted to corroborate network pharmacology findings.ResultsWe discerned 20 oral active compounds within SH003 and identified 239 core targets shared between SH003 and NSCLC-related genes. Network analyses spotlighted 79 hub genes, including TP53, JUN, AKT1, STAT3, and MAPK3, crucial in NSCLC treatment. GO and KEGG analyses underscored SH003’s multifaceted anti-NSCLC effects from a genetic perspective. Experimental validations verified SH003’s impact on NSCLC cell viability and the downregulation of hub genes. LC–MS analysis confirmed the presence of four active compounds, namely hispidulin, luteolin, baicalein, and chrysoeriol, among the eight compounds with a median of > 10 degrees in the herb-compounds-targets network in SH003. Previously unidentified targets like CASP9, MAPK9, and MCL1 were unveiled, supported by existing NSCLC literature, enhancing the pivotal role of empirical validation in network pharmacology.ConclusionOur study pioneers the harmonization of theoretical predictions with practical validations. Empirical validation illuminates specific SH003 compounds within NSCLC, simultaneously uncovering novel targets for NSCLC treatment. This integrated strategy, accentuating empirical validation, establishes a paradigm for in-depth herbal medicine exploration. Furthermore, our network pharmacology study unveils fresh insights into SH003’s multifaceted molecular mechanisms combating NSCLC. Through this approach, we delineate active compounds of SH003 and target pathways, reshaping our understanding of its therapeutic mechanisms in NSCLC treatment.

Static and dynamic fMRI-derived functional connectomes represent largely similar information.

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Reduced Cortico-Cortical Resting-State Connectivity in Sensory Systems Related to Bodily Pain in Juvenile Fibromyalgia.

Juvenile-onset fibromyalgia (JFM) is a paradigmatic chronic pain condition for which the underlying neurobiological substrates are poorly understood. This study examined, for the first time, data-driven resting-state functional connectivity (rsFC) alterations in 37 female adolescents with JFM compared with 43 healthy female adolescents and identified associations with bodily pain. Whole-brain voxel-wise rsFC alterations were assessed using the intrinsic connectivity contrast, a measure of node centrality at each voxel, and seed-based analyses for interpretability. We studied the relationship between rsFC alterations in somatosensory systems and the location and extension of bodily pain. Adolescents with JFM had voxel-wise rsFC reductions in the paracentral lobule (PCL)/primary somatosensory cortex (S1) (T=4.89, family-wise error corrected p-value (pFWE)< 0.001) and left midcingulate cortex (T=4.67, pFWE=0.043). Post hoc analyses revealed reduced rsFC spanning major cortical sensory hubs (T > 4.4, pFWE < 0.030). Cortico-cortical rsFC reductions within PCL/S1 in JFM occurred in locations innervated by bodily areas where the pain was most frequent (F=3.15; positive false discovery rate=0.029) and predicted widespread pain (T > 4.4, pFWE < 0.045). Conversely, adolescents with JFM had increases in PCL/S1-thalamus (T=4.75, pFWE=0.046) and PCL/S1-anterior insula rsFC (T=5.13, pFWE=0.039). Reduced cortico-cortical sensory integration involving PCL/S1 and spanning the sensory systems may underly critical pain sensory features in youth with JFM. Reduced sensory integration is paralleled by augmented cross-talk between sensory and affective/salience-processing regions, potentially indicating a shift toward more affectively colored sensory experiences to the detriment of specific sensory discrimination.

A new measure of node centrality on schedule-based space-time networks for the designation of spread potential

Node centrality is one of the most frequently revisited network theoretical concepts, which got many calculation method alternatives, each of them being conceived on different empirical or theoretical network abstractions. The vast majority of centrality measures produced up to date were conceived on static network abstractions (the so-called “snapshot” networks), which arguably are less realistic than dynamic (temporal) network abstractions. The new, temporal node centrality measure that we offer with this article, is based on an uncommon abstraction, of a space-time network derived from service schedules (timetables). The proposed measure was designed to rank nodes of a space-time network based on their spread or transmission potential, and was subsequently implemented on the network of sea ferry transportation derived from the aggregated schedules for sea ferry liner shipping services in Europe, as they occurred in the month of August, 2015. The main feature of our measure, named “the Spread Potential”, is the evaluation of the potential of a node in the network for transmitting disease, information (e.g. rumours or false news), as well as other phenomena, whichever support a space-time network abstraction from regular and scheduled services with some known carrying capacities. Such abstractions are, for instance, of the transportation networks (e.g. of airline or maritime shipping or the wider logistics (delivery) networks), networks of medical (hospital) services, educational (teaching) services, and virtually, of any other scheduled networked phenomenon. The article also offers the perspectives of the measure’s applicability on the non-scheduled space-time network abstractions.

Random Walks-Based Node Centralities to Attack Complex Networks

Investigating the network response to node removal and the efficacy of the node removal strategies is fundamental to network science. Different research studies have proposed many node centralities based on the network structure for ranking nodes to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks and is an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node centralities for network dismantling with effective node removal strategies from the literature, namely betweenness, closeness, degree, and k-shell node removal, for synthetic and real-world networks. We evaluate the dismantling of the network by using the size of the largest connected component (LCC). We find that the degree nodes attack is the best strategy overall, and the new node removal strategies based on RW show the highest efficacy in regard to peculiar network topology. Specifically, RW strategy based on covering time emerges as the most effective strategy for a synthetic lattice network and a real-world road network. Our results may help researchers select the best node attack strategies in a specific network class and build more robust network structures.

Building Multiple Classifier Systems Using Linear Combinations of Reduced Graphs

Despite great efforts done in research in the last decades, the classification of general graphs, i.e., graphs with unconstrained labeling and structure, remains a challenging task. Due to the inherent relational structure of graphs it is difficult, or even impossible, to apply standard pattern recognition methods to graphs to achieve high recognition accuracies. Common methods to solve the non-trivial problem of graph classification employ graph matching in conjunction with a distance-based classifier or a kernel machine. In the present paper, we address the specific task of graph classification by means of a novel framework that uses information acquired from a broad range of reduced graph subspaces. Our novel approach can be roughly divided into three successive steps. In the first step, differently reduced graphs are created out of the original graphs relying on node centrality measures. In the second step, we compute the graph edit distance between each reduced graph and all the other graphs of the corresponding graph subspace. Finally, we linearly combine the distances in the third step and feed them into a distance-based classifier to obtain the final classification result. On six graph data sets, we empirically confirm that the proposed multiple classifier system directly benefits from the combined distances computed in the various graph subspaces.

SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality

Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considering the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node’s self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node’s self-influence; in the local neighborhood, the node’s one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node’s local influence; the topological position of the node in the network and the shortest path between nodes are considered to measure the node’s global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible–infected–recovered (SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.

Naturalistic observations of multiteam interaction networks: Implications for cognition in crisis management teams

Interaction has been recognised as an essential lens to understand how cognition is formed in a complex adaptive team such as a multidisciplinary crisis management team (CMT). However, little is known about how interactions within and across CMTs give rise to the multi-team system’s overall cognitive functioning, which is essential to avoid breakdowns in coordination. To address this gap, we characterise and compare the component CMTs’ role-as-intended (RAI) and role-as-observed (RAO) in adapting to the complexity of managing informational needs. To characterise RAI, we conducted semi-structured interviews with subject matter experts and then made a qualitative synthesis using a thematic analysis method. To characterise RAO, we observed multiteam interaction networks in real-time at a simulated training environment and then analysed the component CMTs’ relative importance using node centrality measures. The resulting inconsistencies between RAI and RAO imply the need to investigate cognition in multiple CMTs through the lens of interaction. Practitioner summary: When a disaster occurs, multidisciplinary CMTs are expected to serve their roles as described in written or verbal guidelines. However, according to our naturalistic observations of multiteam interaction networks, such descriptions may be (necessary but) insufficient for designing, training, and evaluating CMTs in the complexity of managing informational needs together.

Branching processes reveal influential nodes in social networks

Branching processes are discrete-time stochastic processes which have been largely employed to model and simulate information diffusion processes over large online social networks such as Twitter and Reddit. Here we show that a variant of the branching process model enables the prediction of the popularity of user-generated content and thus can serve as a method for ranking search results or suggestions displayed to users. The proposed branching-process variant is able to evaluate the importance of an agent in a social network and, thus we propose a novel centrality index, called the Stochastic Potential Gain (SPG). The SPG is the first centrality index which combines the knowledge of the network topology with a dynamic process taking place on it which we call a graph-driven branching process. SPG generalises a range of popular network centrality metrics such as Katz' and Subgraph. We formulate a Monte Carlo algorithm (called MCPG) to compute the SPG and prove that it is convergent and correct. Experiments on two real datasets drawn from Facebook and GitHub demonstrate that MCPG traverses only a small fraction of nodes to produce its result, thus making the Stochastic Potential Gain an appealing option to compute node centrality measure for Online social networks.

Structural differences in the gut microbiome of bats using terrestrial vs. aquatic feeding resources

Bat gut microbiomes are adapted to the specific diets of their hosts. Despite diet variation has been associated with differences in bat microbiome diversity, the influence of diet on microbial community assembly have not been fully elucidated. In the present study, we used available data on bat gut microbiome to characterize the microbial community assembly of five selected bat species (i.e., Miniopterus schreibersii, Myotis capaccinii, Myotis myotis, Myotis pilosus, and Myotis vivesi), using network analysis. These bat species with contrasting habitat and food preferences (i.e., My. capaccinii and My. pilosus can be piscivorous and/or insectivorous; Mi. schreibersii and My. myotis are exclusively insectivorous; while My. vivesi is a marine predator) offer an invaluable opportunity to test the impact of diet on bat gut microbiome assembly. The results showed that My. myotis showed the most complex network, with the highest number of nodes, while My. vivesi has the least complex structured microbiome, with lowest number of nodes in its network. No common nodes were observed in the networks of the five bat species, with My. myotis possessing the highest number of unique nodes. Only three bat species, My. myotis, My. pilosus and My. vivesi, presented a core microbiome and the distribution of local centrality measures of nodes was different in the five networks. Taxa removal followed by measurement of network connectivity revealed that My. myotis had the most robust network, while the network of My. vivesi presented the lowest tolerance to taxa removal. Prediction of metabolic pathways using PICRUSt2 revealed that Mi. schreibersii had significantly higher functional pathway’s richness compared to the other bat species. Most of predicted pathways (82%, total 435) were shared between all bat species, while My. capaccinii, My. myotis and My. vivesi, but no Mi. schreibersii or My. pilosus, showed specific pathways. We concluded that despite similar feeding habits, microbial community assembly can differ between bat species. Other factors beyond diet may play a major role in bat microbial community assembly, with host ecology, sociality and overlap in roosts likely providing additional predictors governing gut microbiome of insectivorous bats.

Monte Carlo Methods for Estimating the Diagonal of a Real Symmetric Matrix

For real symmetric matrices that are accessible only through matrix vector products, we present Monte Carlo estimators for computing the diagonal elements. Our probabilistic bounds for normwise absolute and relative errors apply to Monte Carlo estimators based on random Rademacher, sparse Rademacher, and normalized and unnormalized Gaussian vectors and to vectors with bounded fourth moments. The novel use of matrix concentration inequalities in our proofs represents a systematic model for future analyses. Our bounds mostly do not depend explicitly on the matrix dimension, target different error measures than existing work, and imply that the accuracy of the estimators increases with the diagonal dominance of the matrix. Applications to derivative-based global sensitivity metrics and node centrality measures in network science corroborate this, as do numerical experiments on synthetic test matrices. We recommend against the use in practice of sparse Rademacher vectors, which are the basis for many randomized sketching and sampling algorithms, because they tend to deliver barely a digit of accuracy even under large sampling amounts.

Centrality measure and visualization technique for multiple-parent nodes of earthquakes based on correlation-metric

In this paper, we address the problem of earthquake declustering, and propose a k-nearest neighbors approach based on the selection of multiple-parent nodes with respect to each of the given earthquakes, which can be regarded as a natural extension of the conventional correlation-metric method based on the selection of a single-parent node. Based on this approach, we develop a centrality measure that exploits link weight assigned by a logarithmic-distance scheme and a technique of individually visualizing each set of child nodes with respect to given target earthquakes. For experimental evaluation, we used an earthquake catalog covering Japan and selected 24 earthquakes that caused considerable damage or casualties. We first show that our proposed centrality measure using a logarithmic-distance scheme can rank these 24 major earthquakes higher than four link-weighting schemes (i.e., uniform, magnitude, inverse-distance, and normalized-inverse-distance weighting) and conventional single-parent selection. We then show that unlike the conventional approach to simultaneously visualizing all the events in the catalog, our proposed technique can produce a naturally interpretable classification result for these 24 major earthquakes, by individually visualizing each set of the first to k-th child nodes with different colored markers plotted in the directly interpretable spatio and temporal metrics. As a consequence, we confirm that our approach based on multiple-parent selection is vital and promising.

Interconnected sub-networks of the macaque monkey gustatory connectome.

Macroscopic taste processing connectivity was investigated using functional magnetic resonance imaging during the presentation of sour, salty, and sweet tastants in anesthetized macaque monkeys. This examination of taste processing affords the opportunity to study the interactions between sensory regions, central integrators, and effector areas. Here, 58 brain regions associated with gustatory processing in primates were aggregated, collectively forming the gustatory connectome. Regional regression coefficients (or β-series) obtained during taste stimulation were correlated to infer functional connectivity. This connectivity was then evaluated by assessing its laterality, modularity and centrality. Our results indicate significant correlations between same region pairs across hemispheres in a bilaterally interconnected scheme for taste processing throughout the gustatory connectome. Using unbiased community detection, three bilateral sub-networks were detected within the graph of the connectome. This analysis revealed clustering of 16 medial cortical structures, 24 lateral structures, and 18 subcortical structures. Across the three sub-networks, a similar pattern was observed in the differential processing of taste qualities. In all cases, the amplitude of the response was greatest for sweet, but the network connectivity was strongest for sour and salty tastants. The importance of each region in taste processing was computed using node centrality measures within the connectome graph, showing centrality to be correlated across hemispheres and, to a smaller extent, region volume. Connectome hubs exhibited varying degrees of centrality with a prominent leftward increase in insular cortex centrality. Taken together, these criteria illustrate quantifiable characteristics of the macaque monkey gustatory connectome and its organization as a tri-modular network, which may reflect the general medial-lateral-subcortical organization of salience and interoception processing networks.