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You may also take a look at the source code.
The network in this dataset can be loaded directly from graph-tool with:
import graph_tool.all as gt
g = gt.collection.ns["plant_pol_kato"]

plant_pol_kato — Kato plant-pollinator web

Description

A bipartite network of plants and pollinators from Kyoto University Forest of Ashu, Japan, from 1984 to 1987. Edge weights represent frequency of interactions1


  1. Description obtained from the ICON project. ↩

Tags
Biological Food web Weighted
Citation
  • M. Kato et al., "Insect-flower relationship in the primary beech forest of Ashu, Kyoto: an overview of the flowering phenology and seasonal pattern of insect visits." Contr. Biol. Lab. Kyoto Univ. 27, 309-375 (1990), http://hdl.handle.net/2433/156101
Upstream URL OK
https://iwdb.nceas.ucsb.edu/html/kato_1990.html
Networks
Tip: hover your mouse over a table header to obtain a legend.
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv
plant_pol_kato 772 1,206 3.12 9.25 9.60 10.02 -0.30 0.00 8 0.99 Undirected Bipartite idx pollinator pol_ge_sp plant_ge plant_sp count 23 KiB 30 KiB 26 KiB 24 KiB
Ridiculograms*
None drawing
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.