Python module for applying readability metrics to network and graph visualizations. This project is a work in progress that is being developed by Philip Mathieu (MS DS Student) as part of the Research Apprenticeship program at Northeastern University's Khoury College of Computer Science.
import networkx as nx
import graphreadability as gr
# Create a basic graph using NetworkX
G = nx.Graph()
G.add_nodes_from(
[
(1, {"x": 1, "y": 1}),
(2, {"x": -1, "y": 1}),
(3, {"x": -1, "y": -1}),
(4, {"x": 1, "y": -1}),
(5, {"x": 2, "y": 1}),
]
)
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 2), (1, 3)])
# Create a MetricsSuite to calculate readability metrics
M = gr.MetricsSuite(G)
M.calculate_metrics()
M.pretty_print_metrics()This utility is a python package using matplotlib to show and image and allowing the user to click to add nodes, right click to delete nodes, and click two nodes sequentially to add edges.
python graphreadability/utils/digitize_graphs.py -h
usage: digitize_graphs.py [-h] [-i IMAGE] [-o OUTPUT]
Create a graph from an image.
options:
-h, --help show this help message and exit
-i IMAGE, --image IMAGE
Path to the image to create a graph from.
-o OUTPUT, --output OUTPUT
Path to save the graph to.Metric definitions are derived from:
- C. Dunne, S. I. Ross, B. Shneiderman, and M. Martino. "Readability metric feedback for aiding node-link visualization designers," IBM Journal of Research and Development, 59(2/3) pages 14:1--14:16, 2015.
Initial inspiration was taken from rpgove/greadability.js.
Code in graphreadability/metrics/ is in part derived from code originally published at https://github.com/gavjmooney/graph_metrics/ associated with the following publication:
@Conference{citekey,
author = "Gavin J. Mooney, Helen C. Purchase, Michael Wybrow, Stephen G. Kobourov",
title = "The Multi-Dimensional Landscape of Graph Drawing Metrics",
booktitle = "2024 IEEE 17th Pacific Visualization Symposium (PacificVis)",
year = "2024",
}
Apache 2.0 - see LICENSE.txt