Gist d41ea27d12bc9ff61b37319170e10898

100%

Gist d41ea27d12bc9ff61b37319170e10898

✓ Published0🌍 Public

Last edited Sep 3, 2017

Created on Sep 2, 2017

This example visualizes hierarchical clustering of a brain connectivity graph using a force-directed layout. It loads a Desikan parcellation graph from a GraphML file, computes linkage clusters with scipy’s `linkage` and `fcluster`, and encodes cluster memberships at six levels into node names. The interactive rendering, embedded via an iframe from CodePen, uses d3.forceSimulation to display the nodes and their connections, with node sizes reflecting connection counts and names combining cluster labels and anatomical region names.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 5075781b66de8d9e3eb0d804ce180a58

This visualization displays a brain connectivity network as a node-link diagram, where each node represents a cortical region labeled with Desikan-Killiany atlas terminology (e.g., lh-bankssts, rh-precentral) and sized by its import count. Edges connect regions based on structural connections, with the layout revealing clusters of functionally related brain areas. The visualization uses a hierarchical naming convention to organize regions by hemisphere and lobe, with connection strengths implied by the size values shown in the JSON data. The interactive graph allows exploration of the brain's structural connectivity patterns across 68 cortical regions.

Aakeshavan

81% match

Loading thumbnail…

Gist f64d40bf6e1e0e370d9b2ad12e696c45

A force-directed graph visualizes module dependencies from brain imaging data, where each node represents a cortical region and links show import relationships. The network is computed using a force layout, with node size encoding degree (module size) and colors distinguishing different anatomical lobes. Hovering highlights connections, and the graph can be explored interactively to reveal the hierarchical structure of brain connectivity.

Aakeshavan

80% match

Loading thumbnail…

Gist f8b83c150e82b21c7d6fcadc802fb986

This example visualizes a network of interconnected nodes and links, revealing the structure of a graph through spatial clustering and connective pathways. It uses a force-directed layout powered by `d3.forceSimulation` to position nodes, with `d3.drag` enabling interactive repositioning. The rendering relies on SVG elements for both nodes and links, with node fill colors and radii encoding categorical data and degree. The underlying dataset is a static JSON structure of nodes and edges, drawn using standard SVG `circle` and `line` primitives within a responsive viewBox.

PPeleke

77% match

Loading thumbnail…

Gist 55ada79d9d2de1b08a4437b81a540f9f

A force-directed graph visualization displays a network of brain connectivity nodes, where each node represents a region labeled with hierarchical identifiers and edges are weighted by correlation values. The graph uses a circular layout with curved links connecting nodes, with edge thickness and color mapping to the numerical weights, which include both positive and negative correlations. The visualization, titled "DMN connectogram," is designed to show the default mode network connectivity patterns, with nodes positioned around a circle and linked by curved bezier edges that encode connection strength. Node sizes vary based on their degree of connectivity, and the layout emphasizes the modular structure of the network through spatial grouping. The negative weights (e.g., -1.46, -2.46) suggest inhibitory or anticorrelated connections, while positive weights indicate excitatory links, providing a comprehensive view of the network's functional architecture.

Aakeshavan

76% match

Loading thumbnail…

Gist c7d658714f20f14abd2bcabc44577b36

This visualization is a force-directed graph showing the modular structure of brain connectivity data from a diffusion-weighted imaging (DWI) session. The JSON file describes nodes (brain regions) and their directed connections, encoded using hierarchical identifiers that group regions into modules. Node size represents degree (number of connections), and the network layout arranges nodes spatially to reveal clusters of densely interconnected regions. The graph emphasizes the modular organization of brain connectivity, with module membership visually separable by the hierarchical naming pattern and connection density.

Aakeshavan

76% match

Loading thumbnail…

Gist 8c07c4405dfafcf9d164e5f519f62a05

This example visualizes categorical groupings through a force-directed network layout, using node positions and connecting edges to reveal clusters and their interconnections. The code employs D3.js’s `d3.forceSimulation` for physics-based layout and `d3.geoPath` to render curved links, while node styling relies on the Canvas API’s `getImageData` for pixel-level detail. Data originates from a Gist, processed as JSON. The scene updates each tick, smoothly adjusting to simulated forces, which portrays the structural relationships among items.

PPeleke

76% match

Loading thumbnail…

Gist dbec8851e522a7b2af374affa4f5c152

This example shows a force-directed network diagram of interconnected nodes, colored and sized to reveal clustering and degree differences. The visualization uses D3.js’s `d3.forceSimulation` with collision, link, and many-body forces to position the nodes, while `d3.drag` enables user interaction. Data is loaded from a JSON file containing node and link arrays. The rendering uses SVG circles and lines, with node fill colors determined by a categorical scale, and the simulation updates positions each tick. No additional analysis or animation beyond the basic force layout is present.

Aandrei-antal

75% match

Loading thumbnail…

Gist 5ba4f4c088bc7690ffc7cc7417736203

The visualization displays resting-state fMRI data across seven brain networks, showing how functional connectivity patterns are organized in the brain. It uses a force-directed layout to position nodes representing brain regions, with edges indicating connectivity strength between them. The code relies on numpy and scipy for data processing, while the rendering pipeline employs matplotlib for generating the network plot, with pylab enabling interactive plotting in a Jupyter notebook environment.

Aakeshavan

75% match