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27 results for “Multilayer Network”
Network properties data and code used in "Ecological plasticity governs ecosystem services in multilayer networks".
<p>Code and network properties data used in the analyses presented in "Ecological plasticity governs ecosystem services in multilayer networks". Further information can be requested of the author David A. Bohan (David.Bohan@inrae.fr).</p>
Node-layer duality in networked systems: multilayer brain networks dataset
<p>This dataset contains the processed data used in<a href="https://doi.org/10.1038/s41467-024-50176-5"> Presigny, Corsi and De Vico Fallani (2024)</a>. It is made available for replication purposes. The code to treat these data is available <a href="https://github.com/Presigny/multilayer_duality">here</a>.</p> <p>Multilayer brain networks are obtained from the experimental data published in <a>Guillon <em>et al. </em>(2017)</a>. 23 Alzheimer’s diseased (AD) patients and 27 healthy age-matched control (HC) subjects, participated in the study. For each subject, 6 minutes resting-state eyes-closed brain activity was recorded noninvasively using a whole-head MEG system with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX MEG system) at a sampling rate of 1000Hz. Signal artefacts were removed using different techniques including removed signal space separation, principal component analysis, and visual inspection. Finally, source-imaging was used to project the signals from the sensor to the source space consisting of N = 70 regions of interest (ROI) defined by the Lausanne cortical atlas parcellation (see file name_of_ROIs.txt for the order). Here, we used spectral bicoherence to estimate functional connectivity between ROIs and between frequencies of brain activity. Specifically, we considered M = 77 layers corresponding to frequencies in the 2 − 40 Hz range with a resolution of 0.5 Hz. Other parameters were non overlapping windows of 2s averaged according to the Welch method. The resulting networks are full-multilayer consisting of both intralayer and interlayer connections, including weighted links between replica nodes.</p>
Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light
<p>Data and Code required for reproduction of results in "Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light|</p>
Image sensing with multilayer, nonlinear optical neural networks
<p>This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”.</p> <p>This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper:</p> <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>
Supplementary Materials: The molecular landscape of premature aging diseases defined by multilayer network exploration
<p>This dataset contains supplementary materials related to study "The molecular landscape of premature aging diseases defined by<br>multilayer network exploration" (Beust C., Valdeolivas A., Baptista A., Brière G., Lévy N., Ozisik O., Baudot A.)</p> <p>Supplementary Figures: <br>Supplementary Figure S1: Clustering of the premature aging disease communities obtained with 50 iterations of itRWR <br>Supplementary Figure S2: Clustering of the premature aging disease communities obtained with 150 iterations of itRWR <br>Supplementary Figure S3: Clustering of the premature aging disease communities obtained with 100 iteration of itRWR, with a cutoff value at 0.5 on the dendrogram </p> <p>Supplementary Tables: <br>Supplementary Table S1: Premature Aging HPO phenotypes <br>Supplementary Table S2:Tthe 67 PA diseases and their 132 associated genes from ORPHANET <br>Supplementary Table S3: Number of nodes, edges, and densities of the 4 network layers composing the multilayer biological network <br>Supplementary Table S4: Parameters used for MultiXrank </p> <p>Supplementary Files: <br>Supplementary File S1: Csv file containing the enrichment analysis results computed using the genes associated with physiological aging <br>Supplementary File S2: Excel file containing the gene nodes belonging to each of the 67 communities <br>Supplementary File S3: Excel file containing the gene nodes belonging to each cluster (i.e., the union of the genes belonging to the set of communities composing the cluster) <br>Supplementary File S4: Csv file containing the diseases belonging to each cluster <br>Supplementary File S5: Excel file containing the enrichment of clusters using lists of physiological aging genes <br>Supplementary File S6: Csv file containing the lists of genes differentially expressed in human blood, skin, brain, muscle and breast during aging, from the study of Irizar et al. <br>Supplementary File S7: Excel file containing the enrichment analysis results of the 67 communities, using GO Biological Processes and Cellular Components, and Reactome pathways functional annotations <br>Supplementary File S8: Excel file containing the enrichment analysis results of the 6 clusters, using GO Biological Processes and Cellular Components, and Reactome pathways functional annotations. The last 2 columns contain the seed nodes of the cluster annotated for the corresponding function and their corresponding diseases. <br>Supplementary File S8bis: Excel file containing the enrichment analysis results of the 8 clusters obtained with an alternative cutoff of 0.5 in the dendrogram, using GO Biological Processes and Cellular Components, and Reactome pathways functional annotations.<br>Supplementary File S9: Excel file containing enrichment analysis results of the 6 clusters, using HPO phenotypes </p> <p>Supplementary Text: <br>Supplementary Text S1: Comparison with alternative network exploration strategies, including classical (non- iterative) Random Walk with Restart, Multilayer network partitioning, and computation of network distances with shortest paths.</p> <p> </p> <p> </p>
Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 6. Confusion matrix of multilayer feedforward network
<p>Multilayer feedforward network was used for classification with ANN. In this application, the ANN structure and parameters were obtained after a review of previous studies and very much number of trial runs. There is a total of 10 neurons in the hidden layer in ANN. The Bayesian regularization backpropagation was used for learning algorithm and the mean square error function was also used the performance algorithm. 134 samples in the data set were used for training data, 29 samples were used for validation data, and 29 samples were used for testing data. The confusion matrix obtained at the end of the classification was given in Figure 6. As seen from the confusion matrix, the accuracy of classification 82.8% was achieved in the ANN classification. The ANN performed with a sensitivity of 92.40% and a specificity of 88.82%.</p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 3. Multilayer feed forward network (Gurney, 2004)
<p>Feedforward network is a non-repeating network with the processing units or nodes in the layer, and all nodes in a layer are linked to the nodes of the previous layers. There are different weights on the connection. There is no feedback loop, the signal input can only flow in one direction. Multilayer feedforward network is feedforward ANN concept with multiple weighted layers as seen figure 3. This network is called hidden layers because it has one or more hidden layers between the input and output layers (Gurney, 2004; Tutorials Point, 2017).</p>
Data from: A multilayer network in an herbaceous tropical community reveals multiple roles of floral visitors
<p>Flower visitation does not necessarily mean pollination. In this sense, floral visitors can either act as mutualists (pollinators) or antagonists (floral robbers/thieves), indicating that these interactions are part of a continuum and that a visitor species can present multiple behaviours. We included both mutualistic and antagonistic interactions between plants and floral visitors in a multilayer network to explore the consequences (at the community level) of the dual roles played by flower visitors. The multilayer network of interactions was formed by herbaceous plants (12 species) and insects that visited their flowers (21 species) in an area of Atlantic Forest in Brazil from Jul 2015 to May 2016. The two layers presented similar structures, with high overlap between them. Similar to what was expected, the antagonistic layer was more modular and specialized than the mutualistic layer. Some visitor species exhibited highly central, dual roles, acting as both antagonists and mutualists. Most behaved consistently as mutualists in all their visits, especially bees, which formed a predominantly mutualistic group. Butterflies represented a mixed group in relation to their visits and flies made more antagonistic visits. This research represents an important step towards understanding the role of mutualisms and antagonisms in the structure of interaction networks between herbaceous plants and floral visitors in tropical environments.</p>
Simulation results of routing algorithms for multilayer networks
<p>Most current routing protocols are based on path computation algorithms in graphs (e.g., Dijkstra, Bellman-Ford, etc.). These algorithms have been studied for a long time and are very well understood, both in a centralized and distributed context, as long as they are applied to a network having a single communication protocol. The problem becomes more complex in the multi-protocol case, where there is a possibility of encapsulation of some network protocols into others, therefore inducing nested tunnels. The classic algorithms cited above no longer work in this case because they cannot manage the protocol encapsulations and the corresponding protocol stacks. In this work, we propose a highly parallelizable algorithm that takes into account protocol encapsulations as well as protocol conversions in order to compute shortest paths in a multi-protocol network. To achieve this computation efficiently, we study the transitive closure between subpaths (i.e., the concatenation of two subpaths to obtain a longer one) in the case where each subpath induces a protocol stack, and thus tunnels. Leveraging on Software-Defined Networks with a controller having a highly parallel architecture enables us to compute the routing tables of all nodes in a very efficient way. Experimentation results on both random and realistic topologies show that our algorithm outperforms the previous solutions proposed in the literature.</p>
Data: Core-periphery detection in multilayer networks
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Exploring The Spatial Structure of Interregional Supply Chain: A Multilayer Network Approach
<p><span>This research aims to elucidate the organizational patterns of interregional economic interdependence to enhance our comprehension of the national economy's structure at a regional scale. Employing a multilayer network model, this study represents economic interdependence among Indonesian regions, utilizing the InterRegional Input-Output (IRIO) table. Through the application of various metrics, such as degree and strength distribution, assortativity coefficient, and global and local rich club coefficient, to the multilayer IRIO network, we uncover the organizational patterns of economic exchanges between provinces and economic sectors within Indonesia. Our findings demonstrate that a multilayer network approach reveals the heterogeneous and complex structure of the national economy at the regional level. By analyzing the assortativity pattern and global rich-club coefficient, we illustrate that the IRIO network exhibits a hierarchical organization, where significant provincial-sector nodes are interconnected and form dense rich clubs, extending from a few structural cores to peripheral regions. Additionally, we identify distinct connectivity patterns of non-rich nodes based on their incoming and outgoing relations. The insights gained from this study have implications for the macro-control of regional development.</span></p>
Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"
<p>Supplementary Data for "A framework for the construction of generative models for mesoscale structure in multilayer networks"</p>
Multilayer social networks reveal the social complexity of a cooperatively breeding bird
<p>Focal observations of Arabian babblers (<em>Argya squamiceps</em>). The folder contains adjacency matrices of nine social groups observed between 2017 and 2020. Some of the groups were observed multiple times, the complete list of group rounds is also provided. We recorded six different interaction types, hence the folder contains a total of 114 adjacency matrices.</p>
The structure and ecological function of the interactions between plants and arbuscular mycorrhizal fungi through multilayer networks
<ol> <li>Arbuscular mycorrhizas are one of the most frequent mutualisms in terrestrial ecosystems. Although studies on plant mutualistic interaction networks suggest that they may leave their imprint on plant community structure and dynamics, this has not been explicitly assessed. Thus, in the context of plant-fungi interactions, studies explicitly linking plant-mycorrhizal fungi interaction networks with key ecological functions of plant communities, such as recruitment, are lacking. </li> <li>In this study, we analyse, in two Mediterranean forest communities of southern Iberian Peninsula, how plant-AMF networks modulate plant-plant recruitment interaction networks. We use a new approach integrating plant-AMF and plant recruitment networks into a single multilayer structure. We also develop a new metric (Interlayer Node Neighbourhood Integration, INNI) to explore the impact of a given node on the structure across layers.</li> <li>Similarity of plant species in their AMF communities is positively related to the observed frequency of recruitment interactions in the field. Results reveal that properties of plant-AMF networks, such as plant degree and centrality, contribute to explaining properties of the plant recruitment network, such as in- and out-degree (i.e. sapling bank and canopy service) and its modular structure. However, these relationships differed between the two forest communities. Finally, we identify particular AMF that contribute to integrating the neighbourhood of recruitment interactions between plants.</li> <li>This multilayer network approach is useful to explore the role of plant-AMF interactions on recruitment, a key ecosystem function enhanced by fungi. Results provide evidence that the complex structure of plant-AMF interactions impacts functional and structurally plant-plant interactions, which in turn may potentially influence plant community dynamics, through their effects on the structure of the recruitment network.</li> </ol>
Data of "Multilayer spintronic neural networks with radio-frequency connections"
<p>This dataset corresponds to the open data of the publication <strong>"Multilayer spintronic neural networks with radio-frequency connections"</strong>.</p>
The structure and ecological function of the interactions between plants and arbuscular mycorrhizal fungi through multilayer networks
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Across the edge: Spatial segregation drives community structure in tri-trophic multilayer networks at a forest-grassland edge
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Data from: A multilayer network in an herbaceous tropical community reveals multiple roles of floral visitors
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Data from: The multilayer temporal network of public transport in Great Britain
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.