Examples

Examples#

lightgraph bundles three classic networks so you can try every feature without hunting for data. In Python they live in lightgraph.datasets; in R they are lazy-loaded datasets. Each figure below is live — pan, zoom, hover, and double-click it.

Les Misérables#

Character co-occurrences in Victor Hugo’s novel: 77 characters, 254 weighted edges (the weight counts shared chapters). A perfect size for edge-weight styling — every line is legible. Data from D. E. Knuth, The Stanford GraphBase (1993).

from lightgraph import net_vis, datasets, degree

edges = datasets.les_mis()
net_vis(edges=edges, edge_weight_to_width=True,
        edge_weight_to_opacity=True,
        node_metric=degree(edges, weighted=True),
        metric_size_range=(4, 18))

library(lightgraph)

data(les_mis)
lightgraph(edges = les_mis, edge_weight_to_width = TRUE,
           edge_weight_to_opacity = TRUE,
           node_metric = lg_degree(les_mis, weighted = TRUE),
           metric_size_range = c(4, 18))

A Storm of Swords#

Character interactions in the third book of A Song of Ice and Fire: 107 characters, 352 weighted edges (co-mentions within 15 words). Communities are detected automatically and PageRank drives node size. Data from A. Beveridge and J. Shan, “Network of Thrones”, Math Horizons 23(4) (2016).

from lightgraph import net_vis, datasets, pagerank

edges = datasets.got()
net_vis(edges=edges, node_groups='auto',
        node_metric=pagerank(edges),
        edge_weight_to_width=True)

library(lightgraph)

data(got)
lightgraph(edges = got, node_groups = "auto",
           node_metric = lg_pagerank(got),
           edge_weight_to_width = TRUE)

College football#

Games between 115 Division IA teams in the Fall 2000 season, with each team’s conference as its group — a ground-truth community structure you can compare detection algorithms against. Data from M. Girvan and M. E. J. Newman, PNAS 99 (2002).

from lightgraph import net_vis, datasets

nodes, edges = datasets.football()
net_vis(edges=edges, nodes=nodes)

library(lightgraph)

data(football_edges); data(football_nodes)
lightgraph(nodes = football_nodes, edges = football_edges)

How well does community detection recover the conferences? Compare communities(edges) (Python) or lg_communities(football_edges) (R) against the group column — on this network modularity-based methods recover the conference structure almost perfectly.

For a full feature-by-feature tour on a 19k-edge network, continue to Use lightgraph in Python or Use lightgraph in R.

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