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21 results for “Multiplex Networks”
Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes
<p><strong>Presentation. </strong>For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year.</p> <p>These are the data used in the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” <em>Social Networks</em>, vol. 60, pp. 83–102, 2020. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02082574">hal-02082574</a>⟩</li> </ul> <p><strong>Source code.</strong> The code source is accessible on GitHub: <a href="https://github.com/CompNet/MultiNetVotes">https://github.com/CompNet/MultiNetVotes</a></p> <p><strong>Citation. </strong>If you use these data our this source code, please cite the above paper.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes},</code><br><code> journal = {Social Networks},</code><br><code> year = {2020},</code><br><code> volume = {60},</code><br><code> pages = {83-102},</code><br><code> doi = {10.1016/j.socnet.2019.02.001},</code><br><code>}</code><br><br>----------------------------------------------<br><strong>Details.</strong><br><br><strong># RAW INPUT FILES</strong><br>The 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833<br>The folder structure is as follows:<br>* itsyourparliament/<br>** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.<br>** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)<br>** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs<br><br><strong># ROLLCALL NETWORKS</strong><br>This folder contains two separate zip files regarding rollcall networks:<br>- rollcall-networks: This folder contains only the rollcall networks that are used in the article.<br>- all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data.<br>Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN.<br><br><strong># ROLLCALL PARTITIONS</strong><br>Note that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without any<br>neighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC).<br>The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article.<br><br><strong># ROLLCALL CLUSTERING</strong><br>This folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows:<br>|__ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN.<br>|__ F.purity-k=2-sil=SILHOUETTE_SCORE<br>|__ clu=CLUSTER_NO/<br>|__ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article.<br>|__ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article.<br>----------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Application of hyperbolic geometry of multiplex networks under layer link-based attacks
<p>As real multilayer networks, we consider four networks. The multilayer networks are converted to multiplex networks by assuming that all layers have the same number of nodes (the maximum number of nodes of all layers). Explanation of these networks is as follow:</p> <ol> <li><em>CS-Aarhus_multiplex [1]</em> : The first network used in this study is a 5-layer multiplex network, named CS-Aarhus_multiplex, which has 61 nodes and 620 edges. The multiplex social network consists of five kinds of online and offline relationships (Facebook, Leisure, Work, Co-authorship, Lunch) between the employees of the Computer Science department at Aarhus.</li> <li><em>Data_malaria_PLOSCompBiology_2013 [2]</em>: The second network is a 9-layer multiplex network, which consists of 307 nodes and 35306 edges. Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant.</li> <li>VICKERS CHAN 7th-GRADERS [3] : The third network is a 3-layer multiplex network, called VICKERS CHAN 7th-GRADERS, which includes 29 nodes and 740 edges. The data were collected by Vickers from 29 seventh-grade students in a school in Victoria, Australia. Students were asked to nominate their classmates on several relations including the three layers.</li> </ol> <p> 4. FAO MULTIPLEX TRADE NETWORK [4]: The fourth network is a 364-layer multiplex network, which contains 214 nodes and 318346 edges. We consider different types of trade relationships among countries, obtained from FAO (Food and Agriculture Organization of the United Nations)</p>
Data from: Multiplexed subspaces route neural activity across brain-wide networks
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Asynchronous updates can promote the evolution of cooperation on multiplex networks
<p>Code is included to run the model described for varying enhancement factors, and the different versions of the social dilemmas (public goods game and prisoners dilemma) described in the publication. Code is also included to calculate the payoff probabilities described in the publication. The data used to plot the mean cooperation against the enhancement factors is also included for each of the models permutations. Which code files are for which permutation are described in the accompanying pdf.</p>
The public goods game on multiplex networks
<p>The code and data used to create the figures in chapters 3-7 in the thesis "The public goods game on multiplex networks". The data is labelled for which figure it corresponds to.</p>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
Quantitative Multiplex Immunoprecipitation (QMI) analysis of protein interaction network rearrangements during homeostatic plasticity
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Data from: Link prediction in real-world multiplex networks via layer reconstruction method
Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed Layer Reconstruction Method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.
Multiplexed protein profiling reveals spatial subcellular signaling networks
<ul> <li>Supplementary videos for "<strong>Multiplexed protein profiling reveals spatial subcellular signaling networks</strong>"</li> <li>The videos were made using Volocity 3D visualization software. </li> </ul>
Data from: How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience
Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities' response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., "multiplex networks"), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full "entangled bank" of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.
Data from: How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience
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Data from: Link prediction in real-world multiplex networks via layer reconstruction method
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Multiplex networks reveal geographic constraints on illicit wildlife trafficking
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Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks [CEL-Seq]
GEO Series GSE230533. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing.
Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks III
GEO Series GSE198448. synthetic construct. 4 samples. Type: Other.
Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks II
GEO Series GSE198436. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks IV
GEO Series GSE198724. synthetic construct. 38 samples. Type: Expression profiling by high throughput sequencing.
Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks V
GEO Series GSE198726. Mus musculus. 34 samples. Type: Expression profiling by high throughput sequencing.
Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks VI
GEO Series GSE198727. synthetic construct. 26 samples. Type: Other.
Paired single-cell host profiling with multiplex-tagged bacterial mutants reveals intracellular virulence-immune networks I
GEO Series GSE198433. synthetic construct. 1 samples. Type: Other.
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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.
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DANDI Archive for NWB datasets
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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.