View source: R/centrality-batch8.R
| centrality_entropy_variation | R Documentation |
Ai's (2017) vitality measure: the change in the Shannon entropy of a node-level distribution when a node and its links are removed,
EnV_f(i) = I_f(G) - I_f(G - i), \qquad
I_f(G) = -\sum_j p_j \log p_j, \quad p_j = \frac{f(j)}{\sum_l f(l)},
with f the degree ("entropy_variation_degree", in-, out- or
total degree by mode) or the betweenness
("entropy_variation_betweenness"). Natural logarithm, as in the
author's code. The difference is signed: a positive value means the
remaining network is less even without the node, a negative value that
removing it evens the distribution out. Higher = more important.
centrality_entropy_variation(
x,
of = c("degree", "betweenness"),
mode = "all",
...
)
x |
Network input (matrix, igraph, network, cograph_network, tna object). |
of |
Which distribution: |
mode |
For the degree variant on directed networks: |
... |
Additional arguments passed to |
The degree variant is computed in closed form. The betweenness variant
recomputes betweenness once per node and costs O(n \cdot nm); it
ignores edge weights. Self-loops are counted as igraph counts them. When
a deletion leaves every f at zero (for instance betweenness on a
clique) that entropy is taken as 0.
Validated against the author's own R code path
(iCalEnV() from the paper's repository) to 10^{-15} and
against the quantiles of Table 2 of the paper on its 4234-node
Snake Idioms network.
Named numeric vector, one value per node, in nats.
Ai, X. (2017). Node importance ranking of complex networks with entropy variation. Entropy, 19(7), 303.
centrality for computing multiple measures at once.
star5 <- matrix(0, 5, 5)
star5[1, 2:5] <- 1; star5[2:5, 1] <- 1
rownames(star5) <- colnames(star5) <- LETTERS[1:5]
centrality_entropy_variation(star5)
centrality_entropy_variation(star5, of = "betweenness")
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