Local Metrics Research Articles

Detecting community structure by belonging intensity analysis of intermediate nodes

Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms have been proposed to detect the high-quality communities in real-world networks. However, these algorithms have shortcomings of performing calculation on the whole network or defining objective function and the number of commonties in advance, which affects the performance and complexity of community detection algorithms. In this paper, a novel algorithm has been proposed to detect communities in networks by belonging intensity analysis of intermediate nodes, named BIAS, which is inspired from the interactive behavior in human communication networks. More specifically, intermediate nodes are middlemen between different groups in social networks. BIAS algorithm defines belonging intensity using local interactions and metrics between nodes, and the belonging intensity of intermediate node in different communities is analyzed to distinguish which community the intermediate node belongs to. The experiments of our algorithm with other state-of-the-art algorithms on synthetic networks and real-world networks have shown that BIAS algorithm has better accuracy and can significantly improve the quality of community detection without prior information.

Primary Open Angle Glaucoma Is Associated With Functional Brain Network Reorganization.

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. The recent hypothesis of a brain involvement in primary open angle Glaucoma has sprung interest for neuroimaging studies in this classically ophthalmological pathology.Object: We explored a putative reorganization of functional brain networks in Glaucomatous patients, and evaluated the potential of functional network disruption indices as biomarkers of disease severity in terms of their relationship to clinical variables as well as select retinal layer thicknesses.Methods: Nineteen Glaucoma patients and 16 healthy control subjects (age: 50–76, mean 61.0 ± 8.2 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions using the Automated Anatomical Labeling atlas and adjacency matrices were computed based on partial correlations. Graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates (which use regression of graph-theoretical metrics across subjects to quantify overall network changes) were then generated for all subjects. All subjects also underwent Optical Coherence Tomography (OCT) and visual field index (VFI) quantification. We then examined associations between brain network measures and VFI, as well as thickness of retinal nerve fiber layer (RNFL) and macular ganglion cell layer (MaculaGCL).Results: In Glaucoma, group-wise disruption indices were negative for all graph theoretical metrics. Also, we found statistically significant group-wise differences in subject-wise disruption indexes in all local metrics. Two brain regions serving as hubs in healthy controls were not present in the Glaucoma group. Instead, three hub regions were present in Glaucoma patients but not in controls. We found significant associations between all disruption indices and VFI, RNFL as well as MaculaGCL. The disruption index based on the clustering coefficient yielded the best discriminative power for differentiating Glaucoma patients from healthy controls [Area Under the ROC curve (AUC) 0.91, sensitivity, 100%; specificity, 78.95%].Conclusions: Our findings support a possible relationship between functional brain changes and disease severity in Glaucoma, as well as alternative explanations for motor and cognitive symptoms in Glaucoma, possibly pointing toward an inclusion of this pathology in the heterogeneous group of disconnection syndromes.

A finite element methodology to incorporate kinematic activation of discrete deformation twins in a crystal plasticity framework

A finite element framework is presented in which grains within a polycrystalline microstructure are pre-discretized into lamellar regions that become candidates to deform by twinning. This enables the mapping of the motion due to twinning on geometrically proper regions. At some point in the loading of the polycrystal, a twin is triggered, at which point the nodes within a twin region are rapidly mapped to their twinned location, the region’s crystal lattice is reoriented, and the remainder of the body deforms by means of crystallographic slip to enforce mechanical equilibrium. The framework allows for both the onset of twinning and twin growth, and is intended to serve as a test bed for investigating models proposed for these phenomena. In this paper, the framework is described and simulations are performed to demonstrate its ability to handle the large, fast deformation of twinning within a targeted grain of a polycrystalline aggregate. Various simulations are performed, where the initial twin width and the point of twin activation in the loading history are parameterized. Deformation fields in and around the twinned region are inspected after a twinning event, and changes in local stress states are discussed in light of global and local energetic metrics.

Improved Distance Metrics for Histogram-Based Device-Free Localization

Device-free localization (DFL) systems that rely on the wireless received signal strength indicator (RSSI) metric have been reported in literature for almost a decade. Histogram distance-based DFL (HD-DFL) techniques that operate by constructing RSSI histograms are highly effective as they can localize stationary and moving people in both outdoor and complex indoor environments. A key step in the histogram approaches is the estimation of the difference between the “long-term” and “short-term” histograms. The existing HD-DFL methods use either Kullback–Leibler or the subsequent improvement, kernel distance, to measure this difference. This paper is the first known work to compare an extensive range of histogram distance metrics within a DFL context and demonstrate how a judicious selection of a distance metric can significantly increase the performance of an HD-DFL system. The results from practical implementation in two different environments show that some distance metrics perform considerably better than the kernel distance when used for existing DFL techniques, such as radio tomographic imaging (RTI) and SpringLoc, with the overall median tracking error reducing by up to 25%.

Research on Link Prediction Based on Similarity Index Algorithm

For social network with no rights and no directions, link prediction has lower computational complexity and higher link prediction accuracy by using similarity metrics. The existing local information-based similarity metrics are mostly calculated by the common neighbors of the pair of nodes to be predicted when performing link prediction, but these methods do not consider the influence of the relationship between the common neighbors. Aiming at this problem, this paper proposes a new link prediction algorithm based on the existing local information-based similarity measurement method, which not only considers the number and degree of common neighbors between the pairs of nodes to be predicted, but also considers the relationship between common neighbors. In order to verify the effectiveness of the algorithm, the proposed algorithm is compared with the original algorithm and other methods based on the similarity measure of local information in this paper. The experimental results demonstrate the effectiveness of the algorithm.

The effect of multidirectional wall shear stress on plaque characteristics: delving deeper into local shear stress metrics.

The effect of multidirectional wall shear stress on plaque characteristics: delving deeper into local shear stress metrics.

A study on the friendship paradox \u2013 quantitative analysis and relationship with assortative mixing

The friendship paradox is the observation that friends of individuals tend to have more friends or be more popular than the individuals themselves. In this work, we first study local metrics to capture the strength of the paradox and the direction of the paradox from the perspective of individual nodes, i.e., an indication of whether the individual is more or less popular than its friends. These local metrics are aggregated, and global metrics are proposed to express the phenomenon on a network-wide level. Theoretical results show that the defined metrics are well-behaved enough to capture the friendship paradox. We also theoretically analyze the behavior of the friendship paradox for popular network models in order to understand regimes where friendship paradox occurs. These theoretical findings are complemented by experimental results on both network models and real-world networks. By conducting a correlation study between the proposed metrics and degree assortativity, we experimentally demonstrate that the phenomenon of the friendship paradox is related to the well-known phenomenon of assortative mixing.

Adaptation of an assessment system for establishing a River Physical Quality Index and testing its effectiveness with fish‐based metrics in Malaysia

AbstractThis study shows how the Stream Visual Assessment Protocol Version 2 that originated from United States was readapted to formulate the River Physical Quality Index (RPQI) for Malaysia. The approach emphasizes that it is not necessary to “reinvent the wheel” as globally, there are already a range of established best practices in developed countries to emulate. The proposed RPQI protocol was tested on four eco‐hydrological zones in Kampar River for 18 months to measure and detect temporal and spatial physical impairment induced by natural and anthropogenic factors. To be relevant to the Malaysian context, local fish community structure metrics were deployed to test and validate RPQI sensitivity and effectiveness. The results showed that RPQI values were able to reflect river physical degradation and correlated strongly with most fish‐based metrics. However, the RPQI values were weakly linked to fish population abundance and evenness metrics. The results implied that species niche responses need to be considered when applied for testing river assessment system, particularly when introduced species are present. The novel insights provided herein are constructive to inform similar endeavours in other Southeast Asia tropical countries for testing river physical assessment systems with biological metrics.

Genetic diversity hotspots and refugia identified by mapping multi-plant species haplotype diversity in China

Historical processes during the Quaternary are likely to have left a signature on the geographical distribution of intraspecific genetic variation. In particular, high genetic uniqueness could be expected within glacial refugia for multiple species. We aimed to test this for plants in China and whether multi-species hotspots of genetic diversity are good indicators of glacial refugia in this region. From chloroplast DNA haplotype data for 116 species we calculated two local genetic diversity metrics for each species: haplotype genetic richness and genetic uniqueness. From these two, only uniqueness could reliably identify refugia, whereas richness may indicate either glacial refugia or areas recolonized by genetic lineages from different refugia in the postglacial period. Our results suggest the occurrence of numerous cryptic refugia and their likely importance in the maintenance and evolution of the Chinese flora, and indicate that an approach that locates geographic hotspots of genetic diversity data can reliably identify refugia.

A Relational Theory of Locality

In many areas of program and system analysis and optimization, locality is a common concept and has been defined and measured in many ways. This article aims to formally establish relations between these previously disparate types of locality. It categorizes locality definitions in three groups and shows whether and how they can be interconverted. For the footprint, a recent metric, it gives a new measurement algorithm that is asymptotically more time/space efficient than previous approaches. Using the conversion relations, the new algorithm derives with the same efficiency different locality metrics developed and used in program analysis, memory management, and cache design.

A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

Intrinsic Secrecy in Inhomogeneous Stochastic Networks

Network secrecy is vital for a variety of wireless applications and can be accomplished by exploiting network interference. Recently, interference engineering strategies (IESs) have been developed to harness network interference, depending on the wireless environment (node distribution, transmission policy, and channel conditions). Typically, the node spatial distribution has been modeled according to a homogeneous Poisson point process for mathematical tractability. However, such a model can be inadequate for inhomogeneous (e.g., sensor and vehicular) networks. This paper develops a framework for the design and analysis of inhomogeneous wireless networks with intrinsic secrecy. Based on the characterization of the network interference and received signal-to-interference ratio for different receiver selection strategies. Local and global secrecy metrics are introduced for characterizing the level of intrinsic secrecy in inhomogeneous wireless networks from a link and a network perspective. The benefits of IESs are quantified by simulations in various scenarios, thus corroborating the analysis. Results show that IESs can elevate the network secrecy significantly.

PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline.

Spectral matching sequence database search engines commonly used on mass spectrometry-based proteomics experiments excel at identifying peptide sequence ions, and in addition, possible sequence ions carrying post-translational modifications (PTMs), but most do not provide confidence metrics for the exact localization of those PTMs when several possible sites are available. Localization is absolutely required for downstream molecular cell biology analysis of PTM function in vitro and in vivo. Therefore, we developed PTMProphet, a free and open-source software tool integrated into the Trans-Proteomic Pipeline, which reanalyzes identified spectra from any search engine for which pepXML output is available to provide localization confidence to enable appropriate further characterization of biologic events. Localization of any type of mass modification (e.g., phosphorylation) is supported. PTMProphet applies Bayesian mixture models to compute probabilities for each site/peptide spectrum match where a PTM has been identified. These probabilities can be combined to compute a global false localization rate at any threshold to guide downstream analysis. We describe the PTMProphet tool, its underlying algorithms, and demonstrate its performance on ground-truth synthetic peptide reference data sets, one previously published small data set, one new larger data set, and also on a previously published phosphoenriched data set where the correct sites of modification are unknown. Data have been deposited to ProteomeXchange with identifier PXD013210.

Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain.

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.

Disruption of brain network organization in primary open angle glaucoma.

Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. Recently, the hypothesis of a brain involvement in primary open angle glaucoma has sprung interest for neuroimaging studies in this pathology. The purpose of this study is to evaluate a putative reorganization of brain networks in glaucomatous patients through graph-theoretical measures of integration, segregation and centrality by exploiting a multivariate networks association measure and a recently introduced global and local brain network disruption index. Nineteen glaucoma patients and sixteen healthy control subjects (age: 50 - 76, mean 61 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions (AAL atlas), adjacency matrices were computed based on partial correlations and graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates were generated for all subjects. We found that the group-wise disruption index was negative and statistically different from 0 in for all graph theoretical metrics. Additionally, statistically significant group-wise differences in subject-wise disruption indexes were found in all local metrics. The differences in local network measures highlight cerebral reorganization of brain networks in glaucoma patients, supporting the interpretation of glaucoma as central nervous system disease, likely part of the heterogeneous group of recently described disconnection syndromes.

Compressing animated meshes with fine details using local spectral analysis and deformation transfer

Geometry-centric shape animation, usually represented as dynamic meshes with fixed connectivity and time-deforming geometry, is becoming ubiquitous in digital entertainment and other relevant graphics applications. However, digital animation with fine details, which requires more diversity of texture on meshed geometry, always consumes a significant amount of storage space, and compactly storing and efficiently transmitting these meshes still remain technically challenging. In this paper, we propose a novel key-frame-based dynamic meshes compression method, wherein we decompose the meshes into the low-frequency and high-frequency parts by applying piece-wise manifold harmonic bases to reduce spatial-temporal redundancy of primary poses and by using deformation transfer to recover high-frequency details. First of all, we partition the animated meshes into several clusters with similar poses, and the primary poses of meshes in each cluster can be characterized as a linear combination of manifold harmonic bases derived from the key-frame of that cluster. Second, we recover the geometric details on each primary pose using the deformation transfer technique which reconstructs the details from the key-frames. Thus, we only need to store a very small number of key-frames and a few harmonic coefficients for compressing time-varying meshes, which would reduce a significant amount of storage in contrast with traditional methods where bases were stored explicitly. Finally, we employ the state-of-the-art static mesh compression method to store the key-frames and apply a second-order linear prediction coding to the harmonics coefficients to further reduce the spatial-temporal redundancy. Our comprehensive experiments and thorough evaluations on various datasets have manifested that, our novel method could obtain a high compression ratio while preserving high-fidelity geometry details and guaranteeing limited human perceived distortion rate simultaneously, as quantitatively characterized by the popular Karni–Gotsman error and our newly devised local rigidity error metrics.

Classification of Multiple Sclerosis Clinical Profiles via Graph Convolutional Neural Networks.

Recent advances in image acquisition and processing techniques, along with the success of novel deep learning architectures, have given the opportunity to develop innovative algorithms capable to provide a better characterization of neurological related diseases. In this work, we introduce a neural network based approach to classify Multiple Sclerosis (MS) patients into four clinical profiles. Starting from their structural connectivity information, obtained by diffusion tensor imaging and represented as a graph, we evaluate the classification performances using unweighted and weighted connectivity matrices. Furthermore, we investigate the role of graph-based features for a better characterization and classification of the pathology. Ninety MS patients (12 clinically isolated syndrome, 30 relapsing-remitting, 28 secondary-progressive, and 20 primary-progressive) along with 24 healthy controls, were considered in this study. This work shows the great performances achieved by neural networks methods in the classification of the clinical profiles. Furthermore, it shows local graph metrics do not improve the classification results suggesting that the latent features created by the neural network in its layers have a much important informative content. Finally, we observe that graph weights representation of brain connections preserve important information to discriminate between clinical forms.

Optimization of graph construction can significantly increase the power of structural brain network studies

Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated.The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.

Simultaneous learning of reduced prototypes and local metric for image set classification

Classification based on image set is recently a competitive technique, where each set contains multiple images of a person or an object. As a widely used model, affine hull has shown its power in modeling image set. However, due to the existence of noise and outliers, the over-large affine hull usually matches fails when two hulls overlapped. Aiming at alleviating this handicap, this paper proposes a novel method for image set classification, namely Learning of Reduced Prototypes and Local Metric (LRPLM). Specifically, for each gallery image set, a reduced set of prototypes and an optimal local feature-wise metric are simultaneously learned, which jointly minimize the loss function involved the estimation of classification error probability. In doing so, LRPLM inherits the merits of affine hull with better representation to account for the unseen appearances and makes use of the powerful discriminative ability improved by the local metric. It looks like that LRPLM pulls similar image sets with the same class label “closer” to each other, while pushing dissimilar ones “far away”. Extensive experiments illustrate the considerable effectiveness of LRPLM on three widely used datasets. As we know, classification is a research hotspot in expert and intelligent systems. Different from the previous classification methods, LRPLM focuses on image set-based classification technology, while most of them are single-shot classification technology. Thus, the proposed method can be considered as an expert system technology for medical diagnosis, security monitoring, object categorization, and biometrics recognition applications.

Direct and indirect effects of flood regime on macroinvertebrate assemblages in a floodplain riverscape

AbstractFloods are necessary for the functioning of floodplain ecosystems. Ultimately, flood effects on biota are spatially complex and unfold at various time scales. Yet, biodiversity conservation in regulated floodplains requires a sound quantification of the main pathways how floods affect riverine ecosystems. Here, we investigated the relative effects of flood frequency, magnitude, and time elapsed since the last flood on macroinvertebrate communities of 24 parafluvial habitats in the Maggia river floodplain, Switzerland. Parafluvial habitats were sampled seasonally over 1 year, and a combination of 2D hydrodynamic modelling and piecewise structural equation modelling was used to quantify the direct and indirect effects of local hydrological metrics on macroinvertebrate communities. We found that high flood frequency favoured rheophilic species (EPT taxa) over other groups (Coleoptera, Diptera) and prevented strong competitive interactions and competitive exclusion by maintaining low food resource levels. Importantly, we found that parafluvial habitats exhibited more lentic‐like characteristics as time passed after a flood, which caused EPT taxa to be relatively more abundant and taxa richness higher immediately after flood disturbance. Lastly, overall taxa richness increased over time after a flood and even more so after larger floods. Our results suggest that variation in flood magnitude, frequency, and return period maximizes beta diversity within floodplain riverscapes due to the independent and spatially interactive responses of parafluvial habitats.